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spaCy | spaCy-master/spacy/lang/fr/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
CURRENCY,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
merge_chars,
)
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
ELISION = "' ’".replace(" ", "")
HYPHENS = r"- – — ‐ ‑".replace(" ", "")
_prefixes_elision = "d l n"
_prefixes_elision += " " + _prefixes_elision.upper()
_hyphen_suffixes = "ce clés elle en il ils je là moi nous on t vous"
_hyphen_suffixes += " " + _hyphen_suffixes.upper()
_prefixes = TOKENIZER_PREFIXES + [
r"(?:({pe})[{el}])(?=[{a}])".format(
a=ALPHA, el=ELISION, pe=merge_chars(_prefixes_elision)
)
]
_suffixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
r"(?<=°[FfCcKk])\.", # °C. -> ["°C", "."]
r"(?<=[0-9])%", # 4% -> ["4", "%"]
r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[0-9{al}{e}(?:{q})])\.".format(
al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES
),
r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
r"(?<=[{a}])[{h}]({hs})".format(
a=ALPHA, h=HYPHENS, hs=merge_chars(_hyphen_suffixes)
),
]
)
_infixes = TOKENIZER_INFIXES + [
r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION)
]
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes
| 1,452 | 24.491228 | 68 | py |
spaCy | spaCy-master/spacy/lang/fr/stop_words.py | STOP_WORDS = set(
"""
a à â abord afin ah ai aie ainsi ait allaient allons
alors anterieur anterieure anterieures antérieur antérieure antérieures
apres après as assez attendu au
aupres auquel aura auraient aurait auront
aussi autre autrement autres autrui aux auxquelles auxquels avaient
avais avait avant avec avoir avons ayant
bas basee bat
c' c’ ça car ce ceci cela celle celle-ci celle-la celle-là celles celles-ci celles-la celles-là
celui celui-ci celui-la celui-là cent cependant certain certaine certaines certains certes ces
cet cette ceux ceux-ci ceux-là chacun chacune chaque chez ci cinq cinquantaine cinquante
cinquantième cinquième combien comme comment compris concernant
d' d’ da dans de debout dedans dehors deja dejà delà depuis derriere
derrière des desormais desquelles desquels dessous dessus deux deuxième
deuxièmement devant devers devra different differente differentes differents différent
différente différentes différents dire directe directement dit dite dits divers
diverse diverses dix dix-huit dix-neuf dix-sept dixième doit doivent donc dont
douze douzième du duquel durant dès déja déjà désormais
effet egalement eh elle elle-meme elle-même elles elles-memes elles-mêmes en encore
enfin entre envers environ es ès est et etaient étaient etais étais etait était
etant étant etc etre être eu eux eux-mêmes exactement excepté également
fais faisaient faisant fait facon façon feront font
gens
ha hem hep hi ho hormis hors hou houp hue hui huit huitième
hé i il ils importe
j' j’ je jusqu jusque juste
l' l’ la laisser laquelle le lequel les lesquelles lesquels leur leurs longtemps
lors lorsque lui lui-meme lui-même là lès
m' m’ ma maint maintenant mais malgre malgré me meme memes merci mes mien
mienne miennes miens mille moi moi-meme moi-même moindres moins
mon même mêmes
n' n’ na ne neanmoins neuvième ni nombreuses nombreux nos notamment
notre nous nous-mêmes nouveau nul néanmoins nôtre nôtres
o ô on ont onze onzième or ou ouias ouste outre
ouvert ouverte ouverts où
par parce parfois parle parlent parler parmi partant
pas pendant pense permet personne peu peut peuvent peux plus
plusieurs plutot plutôt possible possibles pour pourquoi
pourrais pourrait pouvait prealable precisement
premier première premièrement
pres procedant proche près préalable précisement pu puis puisque
qu' qu’ quand quant quant-à-soi quarante quatorze quatre quatre-vingt
quatrième quatrièmement que quel quelconque quelle quelles quelqu'un quelque
quelques quels qui quiconque quinze quoi quoique
relative relativement rend rendre restant reste
restent retour revoici revoila revoilà
s' s’ sa sait sans sauf se seize selon semblable semblaient
semble semblent sent sept septième sera seraient serait seront ses seul seule
seulement seuls seules si sien sienne siennes siens sinon six sixième soi soi-meme soi-même soit
soixante son sont sous souvent specifique specifiques spécifique spécifiques stop
suffisant suffisante suffit suis suit suivant suivante
suivantes suivants suivre sur surtout
t' t’ ta tant te tel telle tellement telles tels tenant tend tenir tente
tes tien tienne tiennes tiens toi toi-meme toi-même ton touchant toujours tous
tout toute toutes treize trente tres trois troisième troisièmement très
tu té
un une unes uns
va vais vas vers via vingt voici voila voilà vont vos
votre votres vous vous-mêmes vu vé vôtre vôtres
y
""".split()
)
| 3,403 | 39.047059 | 96 | py |
spaCy | spaCy-master/spacy/lang/fr/syntax_iterators.py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
labels = [
"nsubj",
"nsubj:pass",
"obj",
"obl",
"obl:agent",
"obl:arg",
"obl:mod",
"nmod",
"pcomp",
"appos",
"ROOT",
]
post_modifiers = ["flat", "flat:name", "flat:foreign", "fixed", "compound"]
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_deps = {doc.vocab.strings.add(label) for label in labels}
np_modifs = {doc.vocab.strings.add(modifier) for modifier in post_modifiers}
np_label = doc.vocab.strings.add("NP")
adj_label = doc.vocab.strings.add("amod")
det_label = doc.vocab.strings.add("det")
det_pos = doc.vocab.strings.add("DET")
adp_pos = doc.vocab.strings.add("ADP")
conj_label = doc.vocab.strings.add("conj")
conj_pos = doc.vocab.strings.add("CCONJ")
prev_end = -1
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.dep in np_deps:
right_childs = list(word.rights)
right_child = right_childs[0] if right_childs else None
if right_child:
if (
right_child.dep == adj_label
): # allow chain of adjectives by expanding to right
right_end = right_child.right_edge
elif (
right_child.dep == det_label and right_child.pos == det_pos
): # cut relative pronouns here
right_end = right_child
elif right_child.dep in np_modifs: # Check if we can expand to right
right_end = word.right_edge
else:
right_end = word
else:
right_end = word
prev_end = right_end.i
left_index = word.left_edge.i
left_index = left_index + 1 if word.left_edge.pos == adp_pos else left_index
yield left_index, right_end.i + 1, np_label
elif word.dep == conj_label:
head = word.head
while head.dep == conj_label and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
prev_end = word.i
left_index = word.left_edge.i # eliminate left attached conjunction
left_index = (
left_index + 1 if word.left_edge.pos == conj_pos else left_index
)
yield left_index, word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
| 3,124 | 35.337209 | 88 | py |
spaCy | spaCy-master/spacy/lang/fr/tokenizer_exceptions.py | import re
from ...symbols import ORTH
from ...util import update_exc
from ..char_classes import ALPHA, ALPHA_LOWER
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .punctuation import ELISION, HYPHENS
# not using the large _tokenizer_exceptions_list by default as it slows down the tokenizer
# from ._tokenizer_exceptions_list import FR_BASE_EXCEPTIONS
FR_BASE_EXCEPTIONS = ["aujourd'hui", "Aujourd'hui"]
def upper_first_letter(text):
if len(text) == 0:
return text
if len(text) == 1:
return text.upper()
return text[0].upper() + text[1:]
def lower_first_letter(text):
if len(text) == 0:
return text
if len(text) == 1:
return text.lower()
return text[0].lower() + text[1:]
_exc = {"J.-C.": [{ORTH: "J."}, {ORTH: "-C."}]}
for exc_data in [
{ORTH: "av."},
{ORTH: "janv."},
{ORTH: "févr."},
{ORTH: "avr."},
{ORTH: "juill."},
{ORTH: "sept."},
{ORTH: "oct."},
{ORTH: "nov."},
{ORTH: "déc."},
{ORTH: "apr."},
{ORTH: "Dr."},
{ORTH: "M."},
{ORTH: "Mr."},
{ORTH: "Mme."},
{ORTH: "Mlle."},
{ORTH: "n°"},
{ORTH: "d°"},
{ORTH: "St."},
{ORTH: "Ste."},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"après-midi",
"au-delà",
"au-dessus",
"celle-ci",
"celles-ci",
"celui-ci",
"cf.",
"ci-dessous",
"elle-même",
"en-dessous",
"etc.",
"jusque-là",
"lui-même",
"MM.",
"No.",
"peut-être",
"pp.",
"quelques-uns",
"rendez-vous",
"Vol.",
]:
_exc[orth] = [{ORTH: orth}]
for verb in [
"a",
"est",
"semble",
"indique",
"moque",
"passe",
]:
for orth in [verb, verb.title()]:
for pronoun in ["elle", "il", "on"]:
token = f"{orth}-t-{pronoun}"
_exc[token] = [{ORTH: orth}, {ORTH: "-t"}, {ORTH: "-" + pronoun}]
for verb in ["est"]:
for orth in [verb, verb.title()]:
_exc[f"{orth}-ce"] = [{ORTH: orth}, {ORTH: "-ce"}]
for pre in ["qu'", "n'"]:
for orth in [pre, pre.title()]:
_exc[f"{orth}est-ce"] = [{ORTH: orth}, {ORTH: "est"}, {ORTH: "-ce"}]
for verb, pronoun in [("est", "il"), ("EST", "IL")]:
_exc[f"{verb}-{pronoun}"] = [{ORTH: verb}, {ORTH: "-" + pronoun}]
for s, verb, pronoun in [("s", "est", "il"), ("S", "EST", "IL")]:
_exc[f"{s}'{verb}-{pronoun}"] = [
{ORTH: s + "'"},
{ORTH: verb},
{ORTH: "-" + pronoun},
]
_infixes_exc = [] # type: ignore[var-annotated]
orig_elision = "'"
orig_hyphen = "-"
# loop through the elison and hyphen characters, and try to substitute the ones that weren't used in the original list
for infix in FR_BASE_EXCEPTIONS:
variants_infix = {infix}
for elision_char in [x for x in ELISION if x != orig_elision]:
variants_infix.update(
[word.replace(orig_elision, elision_char) for word in variants_infix]
)
for hyphen_char in [x for x in ["-", "‐"] if x != orig_hyphen]:
variants_infix.update(
[word.replace(orig_hyphen, hyphen_char) for word in variants_infix]
)
variants_infix.update([upper_first_letter(word) for word in variants_infix])
_infixes_exc.extend(variants_infix)
for orth in _infixes_exc:
_exc[orth] = [{ORTH: orth}]
_hyphen_prefix = [
"a[ée]ro",
"abat",
"a[fg]ro",
"after",
"aigues?",
"am[ée]ricano",
"anglo",
"anti",
"apr[èe]s",
"arabo",
"arcs?",
"archi",
"arrières?",
"audio",
"avant",
"avion",
"auto",
"banc",
"bas(?:ses?)?",
"bateaux?",
"bec?",
"belles?",
"beau",
"best",
"bio?",
"bien",
"blanc",
"bo[îi]te",
"bonn?e?s?",
"bois",
"bou(?:c|rg)",
"b[êe]ta",
"cache",
"cap(?:ello)?",
"casse",
"castel",
"champ",
"chapelle",
"ch[âa]teau(?:neuf)?",
"chasse",
"cha(?:ud|t)e?s?",
"chauffe",
"chou",
"chromo",
"claire?s?",
"co(?:de|ca)?",
"compte",
"contre",
"cordon",
"coupe?",
"courte?s?",
"couvre",
"crash",
"crise",
"croche",
"cross",
"cyber",
"côte",
"demi",
"di(?:sney)?",
"dix",
"d[ée]s?",
"dys",
"ex?",
"émirato",
"entre",
"est",
"ethno",
"ex",
"extra",
"extrême",
"[ée]co",
"faux",
"fil",
"fort",
"franco?s?",
"gallo",
"gardes?",
"gastro",
"grande?",
"gratte",
"gr[ée]co",
"gros",
"g[ée]o",
"haute?s?",
"homm?es?",
"hors",
"hyper",
"indo",
"infra",
"inter",
"intra",
"islamo",
"italo",
"jean",
"labio",
"latino",
"live",
"lot",
"louis",
"m[ai]cro",
"mal",
"médio",
"mesnil",
"mi(?:ni)?",
"mono",
"mont?s?",
"moyen",
"multi",
"m[ée]cano",
"m[ée]dico",
"m[ée]do",
"m[ée]ta",
"mots?",
"neuro",
"noix",
"non",
"nord",
"notre",
"n[ée]o",
"ouest",
"outre",
"ouvre",
"passe",
"perce",
"pharmaco",
"ph[oy]to",
"pieds?",
"pique",
"poissons?",
"ponce",
"pont",
"po[rs]t",
"pousse",
"primo",
"pro(?:cès|to)?",
"pare",
"petite?s?",
"plessis",
"porte",
"pré",
"prêchi",
"protège",
"pseudo",
"pêle",
"péri",
"puy",
"quasi",
"quatre",
"radio",
"recourt",
"rythmo",
"(?:re)?doubles?",
"r[ée]",
"r[ée]tro",
"requin",
"sans?",
"sa?inte?s?",
"semi",
"serre",
"sino",
"socio",
"sociale?s?",
"soixante",
"sous",
"su[bdrs]",
"super",
"taille",
"tire",
"thermo",
"tiers",
"tourne",
"toute?s?",
"tra[iî]ne?",
"trans",
"trente",
"trois",
"trousse",
"tr(?:i|ou)",
"t[ée]l[ée]",
"utéro",
"vaso",
"vi[cd]e",
"vid[ée]o",
"vie(?:ux|i?lles?|i?l)",
"vill(?:e|eneuve|ers|ette|iers|y)",
"vingt",
"voitures?",
"wagons?",
"ultra",
"à",
"[ée]lectro",
"[ée]qui",
"Fontaine",
"La Chapelle",
"Marie",
"Le Mesnil",
"Neuville",
"Pierre",
"Val",
"Vaux",
]
_regular_exp = [
"^a[{hyphen}]sexualis[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^arginine[{hyphen}]méthyl[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^binge[{hyphen}]watch[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^black[{hyphen}]out[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^bouche[{hyphen}]por[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^burn[{hyphen}]out[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^by[{hyphen}]pass[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^ch[{elision}]tiis[{al}]+$".format(elision=ELISION, al=ALPHA_LOWER),
"^chape[{hyphen}]chut[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^down[{hyphen}]load[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^[ée]tats[{hyphen}]uni[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^droits?[{hyphen}]de[{hyphen}]l'homm[{al}]+$".format(
hyphen=HYPHENS, al=ALPHA_LOWER
),
"^fac[{hyphen}]simil[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^fleur[{hyphen}]bleuis[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^flic[{hyphen}]flaqu[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^fox[{hyphen}]trott[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^google[{hyphen}]is[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^hard[{hyphen}]discount[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^hip[{hyphen}]hop[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^jet[{hyphen}]set[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^knock[{hyphen}]out[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^lèche[{hyphen}]bott[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^litho[{hyphen}]typographi[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^lock[{hyphen}]out[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^lombri[{hyphen}]compost[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^mac[{hyphen}]adamis[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^marque[{hyphen}]pag[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^mouton[{hyphen}]noiris[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^new[{hyphen}]york[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^pair[{hyphen}]programm[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^people[{hyphen}]is[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^plan[{hyphen}]socialis[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^premier[{hyphen}]ministr[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^prud[{elision}]hom[{al}]+$".format(elision=ELISION, al=ALPHA_LOWER),
"^réarc[{hyphen}]bout[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^refox[{hyphen}]trott[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^remicro[{hyphen}]ond[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^repique[{hyphen}]niqu[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^repetit[{hyphen}]déjeun[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^rick[{hyphen}]roll[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^rond[{hyphen}]ponn[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^shift[{hyphen}]cliqu[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^soudo[{hyphen}]bras[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^stabilo[{hyphen}]boss[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^strip[{hyphen}]teas[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^terra[{hyphen}]form[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^teuf[{hyphen}]teuf[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^yo[{hyphen}]yo[{al}]+$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^zig[{hyphen}]zag[{al}]*$".format(hyphen=HYPHENS, al=ALPHA_LOWER),
"^z[{elision}]yeut[{al}]+$".format(elision=ELISION, al=ALPHA_LOWER),
]
# catching cases like faux-vampire
_regular_exp += [
"^{prefix}[{hyphen}][{al}][{hyphen}{al}{elision}]*$".format(
prefix=p,
hyphen=HYPHENS, # putting the - first in the [] range avoids having to use a backslash
elision=ELISION,
al=ALPHA_LOWER,
)
for p in _hyphen_prefix
]
# catching cases like entr'abat
_elision_prefix = ["r?é?entr", "grande?s?", "r"]
_regular_exp += [
"^{prefix}[{elision}][{al}][{hyphen}{al}{elision}]*$".format(
prefix=p, elision=ELISION, hyphen=HYPHENS, al=ALPHA_LOWER
)
for p in _elision_prefix
]
# catching cases like saut-de-ski, pet-en-l'air
_hyphen_combination = [
"l[èe]s?",
"la",
"en",
"des?",
"d[eu]",
"sur",
"sous",
"aux?",
"à",
"et",
"près",
"saint",
]
_regular_exp += [
"^[{a}]+[{hyphen}]{hyphen_combo}[{hyphen}](?:l[{elision}])?[{a}]+$".format(
hyphen_combo=hc, elision=ELISION, hyphen=HYPHENS, a=ALPHA
)
for hc in _hyphen_combination
]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
TOKEN_MATCH = re.compile(
"(?iu)" + "|".join("(?:{})".format(m) for m in _regular_exp)
).match
| 11,174 | 24.168919 | 118 | py |
spaCy | spaCy-master/spacy/lang/ga/__init__.py | from typing import Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import IrishLemmatizer
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class IrishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
stop_words = STOP_WORDS
class Irish(Language):
lang = "ga"
Defaults = IrishDefaults
@Irish.factory(
"lemmatizer",
assigns=["token.lemma"],
default_config={"model": None, "mode": "pos_lookup", "overwrite": False},
default_score_weights={"lemma_acc": 1.0},
)
def make_lemmatizer(
nlp: Language, model: Optional[Model], name: str, mode: str, overwrite: bool
):
return IrishLemmatizer(nlp.vocab, model, name, mode=mode, overwrite=overwrite)
__all__ = ["Irish"]
| 819 | 23.117647 | 82 | py |
spaCy | spaCy-master/spacy/lang/ga/lemmatizer.py | from typing import Dict, List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class IrishLemmatizer(Lemmatizer):
# This is a lookup-based lemmatiser using data extracted from
# BuNaMo (https://github.com/michmech/BuNaMo)
@classmethod
def get_lookups_config(cls, mode: str) -> Tuple[List[str], List[str]]:
if mode == "pos_lookup":
# fmt: off
required = [
"lemma_lookup_adj", "lemma_lookup_adp",
"lemma_lookup_noun", "lemma_lookup_verb"
]
# fmt: on
return (required, [])
else:
return super().get_lookups_config(mode)
def pos_lookup_lemmatize(self, token: Token) -> List[str]:
univ_pos = token.pos_
string = unponc(token.text)
if univ_pos not in ["PROPN", "ADP", "ADJ", "NOUN", "VERB"]:
return [string.lower()]
demutated = demutate(string)
secondary = ""
if string[0:1].lower() == "h" and string[1:2].lower() in "aáeéiíoóuú":
secondary = string[1:]
lookup_pos = univ_pos.lower()
if univ_pos == "PROPN":
lookup_pos = "noun"
if token.has_morph():
# TODO: lookup is actually required for the genitive forms, but
# this is not in BuNaMo, and would not be of use with IDT.
if univ_pos == "NOUN" and (
"VerbForm=Vnoun" in token.morph or "VerbForm=Inf" in token.morph
):
hpref = "Form=HPref" in token.morph
return [demutate(string, hpref).lower()]
elif univ_pos == "ADJ" and "VerbForm=Part" in token.morph:
return [demutate(string).lower()]
lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
def to_list(value):
if value is None:
value = []
elif not isinstance(value, list):
value = [value]
return value
if univ_pos == "ADP":
return to_list(lookup_table.get(string, string.lower()))
ret = []
if univ_pos == "PROPN":
ret.extend(to_list(lookup_table.get(demutated)))
ret.extend(to_list(lookup_table.get(secondary)))
else:
ret.extend(to_list(lookup_table.get(demutated.lower())))
ret.extend(to_list(lookup_table.get(secondary.lower())))
if len(ret) == 0:
ret = [string.lower()]
return ret
def demutate(word: str, is_hpref: bool = False) -> str:
UVOWELS = "AÁEÉIÍOÓUÚ"
LVOWELS = "aáeéiíoóuú"
lc = word.lower()
# remove eclipsis
if lc.startswith("bhf"):
word = word[2:]
elif lc.startswith("mb"):
word = word[1:]
elif lc.startswith("gc"):
word = word[1:]
elif lc.startswith("nd"):
word = word[1:]
elif lc.startswith("ng"):
word = word[1:]
elif lc.startswith("bp"):
word = word[1:]
elif lc.startswith("dt"):
word = word[1:]
elif word[0:1] == "n" and word[1:2] in UVOWELS:
word = word[1:]
elif lc.startswith("n-") and word[2:3] in LVOWELS:
word = word[2:]
# non-standard eclipsis
elif lc.startswith("bh-f"):
word = word[3:]
elif lc.startswith("m-b"):
word = word[2:]
elif lc.startswith("g-c"):
word = word[2:]
elif lc.startswith("n-d"):
word = word[2:]
elif lc.startswith("n-g"):
word = word[2:]
elif lc.startswith("b-p"):
word = word[2:]
elif lc.startswith("d-t"):
word = word[2:]
# t-prothesis
elif lc.startswith("ts"):
word = word[1:]
elif lc.startswith("t-s"):
word = word[2:]
# h-prothesis, if known to be present
elif is_hpref and word[0:1] == "h":
word = word[1:]
# h-prothesis, simple case
# words can also begin with 'h', but unlike eclipsis,
# a hyphen is not used, so that needs to be handled
# elsewhere
elif word[0:1] == "h" and word[1:2] in UVOWELS:
word = word[1:]
# lenition
# this breaks the previous if, to handle super-non-standard
# text where both eclipsis and lenition were used.
if lc[0:1] in "bcdfgmpst" and lc[1:2] == "h":
word = word[0:1] + word[2:]
return word
def unponc(word: str) -> str:
# fmt: off
PONC = {
"ḃ": "bh",
"ċ": "ch",
"ḋ": "dh",
"ḟ": "fh",
"ġ": "gh",
"ṁ": "mh",
"ṗ": "ph",
"ṡ": "sh",
"ṫ": "th",
"Ḃ": "BH",
"Ċ": "CH",
"Ḋ": "DH",
"Ḟ": "FH",
"Ġ": "GH",
"Ṁ": "MH",
"Ṗ": "PH",
"Ṡ": "SH",
"Ṫ": "TH"
}
# fmt: on
buf = []
for ch in word:
if ch in PONC:
buf.append(PONC[ch])
else:
buf.append(ch)
return "".join(buf)
| 4,889 | 29 | 80 | py |
spaCy | spaCy-master/spacy/lang/ga/stop_words.py | STOP_WORDS = set(
"""
a ach ag agus an aon ar arna as
ba beirt bhúr
caoga ceathair ceathrar chomh chuig chun cois céad cúig cúigear
daichead dar de deich deichniúr den dhá do don dtí dá dár dó
faoi faoin faoina faoinár fara fiche
gach gan go gur
haon hocht
i iad idir in ina ins inár is
le leis lena lenár
mar mo muid mé
na nach naoi naonúr ná ní níor nó nócha
ocht ochtar ochtó os
roimh
sa seacht seachtar seachtó seasca seisear siad sibh sinn sna sé sí
tar thar thú triúr trí trína trínár tríocha tú
um
ár
é éis
í
ó ón óna ónár
""".split()
)
| 567 | 11.909091 | 66 | py |
spaCy | spaCy-master/spacy/lang/ga/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"'acha'n": [{ORTH: "'ach", NORM: "gach"}, {ORTH: "a'n", NORM: "aon"}],
"dem'": [{ORTH: "de", NORM: "de"}, {ORTH: "m'", NORM: "mo"}],
"ded'": [{ORTH: "de", NORM: "de"}, {ORTH: "d'", NORM: "do"}],
"lem'": [{ORTH: "le", NORM: "le"}, {ORTH: "m'", NORM: "mo"}],
"led'": [{ORTH: "le", NORM: "le"}, {ORTH: "d'", NORM: "do"}],
"théis": [{ORTH: "th", NORM: "tar"}, {ORTH: "éis", NORM: "éis"}],
"tréis": [{ORTH: "tr", NORM: "tar"}, {ORTH: "éis", NORM: "éis"}],
}
for exc_data in [
{ORTH: "'gus", NORM: "agus"},
{ORTH: "'ach", NORM: "gach"},
{ORTH: "ao'", NORM: "aon"},
{ORTH: "'niar", NORM: "aniar"},
{ORTH: "'níos", NORM: "aníos"},
{ORTH: "'ndiu", NORM: "inniu"},
{ORTH: "'nocht", NORM: "anocht"},
{ORTH: "m'"},
{ORTH: "Aib."},
{ORTH: "Ath."},
{ORTH: "Beal."},
{ORTH: "a.C.n."},
{ORTH: "m.sh."},
{ORTH: "M.F."},
{ORTH: "M.Fómh."},
{ORTH: "D.F."},
{ORTH: "D.Fómh."},
{ORTH: "r.C."},
{ORTH: "R.C."},
{ORTH: "r.Ch."},
{ORTH: "r.Chr."},
{ORTH: "R.Ch."},
{ORTH: "R.Chr."},
{ORTH: "⁊rl."},
{ORTH: "srl."},
{ORTH: "Co."},
{ORTH: "Ean."},
{ORTH: "Feab."},
{ORTH: "gCo."},
{ORTH: ".i."},
{ORTH: "B'"},
{ORTH: "b'"},
{ORTH: "lch."},
{ORTH: "Lch."},
{ORTH: "lgh."},
{ORTH: "Lgh."},
{ORTH: "Lún."},
{ORTH: "Már."},
{ORTH: "Meith."},
{ORTH: "Noll."},
{ORTH: "Samh."},
{ORTH: "tAth."},
{ORTH: "tUas."},
{ORTH: "teo."},
{ORTH: "Teo."},
{ORTH: "Uas."},
{ORTH: "uimh."},
{ORTH: "Uimh."},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in ["d'", "D'"]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 1,868 | 24.958333 | 74 | py |
spaCy | spaCy-master/spacy/lang/grc/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class AncientGreekDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
suffixes = TOKENIZER_SUFFIXES
infixes = TOKENIZER_INFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class AncientGreek(Language):
lang = "grc"
Defaults = AncientGreekDefaults
__all__ = ["AncientGreek"]
| 620 | 26 | 82 | py |
spaCy | spaCy-master/spacy/lang/grc/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.grc.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"ἐρᾷ μὲν ἁγνὸς οὐρανὸς τρῶσαι χθόνα, ἔρως δὲ γαῖαν λαμβάνει γάμου τυχεῖν·",
"εὐδαίμων Χαρίτων καὶ Μελάνιππος ἔφυ, θείας ἁγητῆρες ἐφαμερίοις φιλότατος.",
"ὃ μὲν δὴ ἀπόστολος ἐς τὴν Μίλητον ἦν.",
"Θρασύβουλος δὲ σαφέως προπεπυσμένος πάντα λόγον καὶ εἰδὼς τὰ Ἀλυάττης μέλλοι ποιήσειν μηχανᾶται τοιάδε.",
"φιλόπαις δ' ἦν ἐκμανῶς καὶ Ἀλέξανδρος ὁ βασιλεύς.",
"Ἀντίγονος ὁ βασιλεὺς ἐπεκώμαζε τῷ Ζήνωνι",
"αὐτὰρ ὃ δεύτατος ἦλθεν ἄναξ ἀνδρῶν Ἀγαμέμνων ἕλκος ἔχων",
]
| 650 | 35.166667 | 110 | py |
spaCy | spaCy-master/spacy/lang/grc/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
# CARDINALS
"εἷς",
"ἑνός",
"ἑνί",
"ἕνα",
"μία",
"μιᾶς",
"μιᾷ",
"μίαν",
"ἕν",
"δύο",
"δυοῖν",
"τρεῖς",
"τριῶν",
"τρισί",
"τρία",
"τέτταρες",
"τεττάρων",
"τέτταρσι",
"τέτταρα",
"τέτταρας",
"πέντε",
"ἕξ",
"ἑπτά",
"ὀκτώ",
"ἐννέα",
"δέκα",
"ἕνδεκα",
"δώδεκα",
"πεντεκαίδεκα",
"ἑκκαίδεκα",
"ἑπτακαίδεκα",
"ὀκτωκαίδεκα",
"ἐννεακαίδεκα",
"εἴκοσι",
"τριάκοντα",
"τετταράκοντα",
"πεντήκοντα",
"ἑξήκοντα",
"ἑβδομήκοντα",
"ὀγδοήκοντα",
"ἐνενήκοντα",
"ἑκατόν",
"διακόσιοι",
"διακοσίων",
"διακοσιᾶν",
"διακοσίους",
"διακοσίοις",
"διακόσια",
"διακόσιαι",
"διακοσίαις",
"διακοσίαισι",
"διηκόσιοι",
"διηκοσίων",
"διηκοσιέων",
"διακοσίας",
"διηκόσια",
"διηκόσιαι",
"διηκοσίας",
"τριακόσιοι",
"τριακοσίων",
"τριακοσιᾶν",
"τριακοσίους",
"τριακοσίοις",
"τριακόσια",
"τριακόσιαι",
"τριακοσίαις",
"τριακοσίαισι",
"τριακοσιέων",
"τριακοσίας",
"τριηκόσια",
"τριηκοσίας",
"τριηκόσιοι",
"τριηκοσίοισιν",
"τριηκοσίους",
"τριηκοσίων",
"τετρακόσιοι",
"τετρακοσίων",
"τετρακοσιᾶν",
"τετρακοσίους",
"τετρακοσίοις",
"τετρακόσια",
"τετρακόσιαι",
"τετρακοσίαις",
"τετρακοσίαισι",
"τετρακοσιέων",
"τετρακοσίας",
"πεντακόσιοι",
"πεντακοσίων",
"πεντακοσιᾶν",
"πεντακοσίους",
"πεντακοσίοις",
"πεντακόσια",
"πεντακόσιαι",
"πεντακοσίαις",
"πεντακοσίαισι",
"πεντακοσιέων",
"πεντακοσίας",
"ἑξακόσιοι",
"ἑξακοσίων",
"ἑξακοσιᾶν",
"ἑξακοσίους",
"ἑξακοσίοις",
"ἑξακόσια",
"ἑξακόσιαι",
"ἑξακοσίαις",
"ἑξακοσίαισι",
"ἑξακοσιέων",
"ἑξακοσίας",
"ἑπτακόσιοι",
"ἑπτακοσίων",
"ἑπτακοσιᾶν",
"ἑπτακοσίους",
"ἑπτακοσίοις",
"ἑπτακόσια",
"ἑπτακόσιαι",
"ἑπτακοσίαις",
"ἑπτακοσίαισι",
"ἑπτακοσιέων",
"ἑπτακοσίας",
"ὀκτακόσιοι",
"ὀκτακοσίων",
"ὀκτακοσιᾶν",
"ὀκτακοσίους",
"ὀκτακοσίοις",
"ὀκτακόσια",
"ὀκτακόσιαι",
"ὀκτακοσίαις",
"ὀκτακοσίαισι",
"ὀκτακοσιέων",
"ὀκτακοσίας",
"ἐνακόσιοι",
"ἐνακοσίων",
"ἐνακοσιᾶν",
"ἐνακοσίους",
"ἐνακοσίοις",
"ἐνακόσια",
"ἐνακόσιαι",
"ἐνακοσίαις",
"ἐνακοσίαισι",
"ἐνακοσιέων",
"ἐνακοσίας",
"χίλιοι",
"χιλίων",
"χιλιῶν",
"χιλίους",
"χιλίοις",
"χίλιαι",
"χιλίας",
"χιλίαις",
"χίλια",
"χίλι",
"δισχίλιοι",
"δισχιλίων",
"δισχιλιῶν",
"δισχιλίους",
"δισχιλίοις",
"δισχίλιαι",
"δισχιλίας",
"δισχιλίαις",
"δισχίλια",
"δισχίλι",
"τρισχίλιοι",
"τρισχιλίων",
"τρισχιλιῶν",
"τρισχιλίους",
"τρισχιλίοις",
"τρισχίλιαι",
"τρισχιλίας",
"τρισχιλίαις",
"τρισχίλια",
"τρισχίλι",
"μύριοι",
"μύριοί",
"μυρίων",
"μυρίοις",
"μυρίους",
"μύριαι",
"μυρίαις",
"μυρίας",
"μύρια",
"δισμύριοι",
"δισμύριοί",
"δισμυρίων",
"δισμυρίοις",
"δισμυρίους",
"δισμύριαι",
"δισμυρίαις",
"δισμυρίας",
"δισμύρια",
"δεκακισμύριοι",
"δεκακισμύριοί",
"δεκακισμυρίων",
"δεκακισμυρίοις",
"δεκακισμυρίους",
"δεκακισμύριαι",
"δεκακισμυρίαις",
"δεκακισμυρίας",
"δεκακισμύρια",
# ANCIENT GREEK NUMBERS (1-100)
"α",
"β",
"γ",
"δ",
"ε",
"ϛ",
"ζ",
"η",
"θ",
"ι",
"ια",
"ιβ",
"ιγ",
"ιδ",
"ιε",
"ιϛ",
"ιζ",
"ιη",
"ιθ",
"κ",
"κα",
"κβ",
"κγ",
"κδ",
"κε",
"κϛ",
"κζ",
"κη",
"κθ",
"λ",
"λα",
"λβ",
"λγ",
"λδ",
"λε",
"λϛ",
"λζ",
"λη",
"λθ",
"μ",
"μα",
"μβ",
"μγ",
"μδ",
"με",
"μϛ",
"μζ",
"μη",
"μθ",
"ν",
"να",
"νβ",
"νγ",
"νδ",
"νε",
"νϛ",
"νζ",
"νη",
"νθ",
"ξ",
"ξα",
"ξβ",
"ξγ",
"ξδ",
"ξε",
"ξϛ",
"ξζ",
"ξη",
"ξθ",
"ο",
"οα",
"οβ",
"ογ",
"οδ",
"οε",
"οϛ",
"οζ",
"οη",
"οθ",
"π",
"πα",
"πβ",
"πγ",
"πδ",
"πε",
"πϛ",
"πζ",
"πη",
"πθ",
"ϟ",
"ϟα",
"ϟβ",
"ϟγ",
"ϟδ",
"ϟε",
"ϟϛ",
"ϟζ",
"ϟη",
"ϟθ",
"ρ",
]
def like_num(text):
if text.lower() in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 4,587 | 13.611465 | 36 | py |
spaCy | spaCy-master/spacy/lang/grc/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_CURRENCY,
LIST_ELLIPSES,
LIST_ICONS,
LIST_PUNCT,
LIST_QUOTES,
)
_prefixes = (
[
"†",
"⸏",
]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_CURRENCY
+ LIST_ICONS
)
_suffixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_ICONS
+ [
"†",
"⸎",
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
]
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])—",
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes
| 1,063 | 18 | 68 | py |
spaCy | spaCy-master/spacy/lang/grc/stop_words.py | STOP_WORDS = set(
"""
αὐτῷ αὐτοῦ αὐτῆς αὐτόν αὐτὸν αὐτῶν αὐτὸς αὐτὸ αὐτό αὐτός αὐτὴν αὐτοῖς αὐτοὺς αὔτ' αὐτὰ αὐτῇ αὐτὴ
αὐτὼ αὑταὶ καὐτὸς αὐτά αὑτός αὐτοῖσι αὐτοῖσιν αὑτὸς αὐτήν αὐτοῖσί αὐτοί αὐτοὶ αὐτοῖο αὐτάων αὐτὰς
αὐτέων αὐτώ αὐτάς αὐτούς αὐτή αὐταί αὐταὶ αὐτῇσιν τὠυτῷ τὠυτὸ ταὐτὰ ταύτῃ αὐτῇσι αὐτῇς αὐταῖς αὐτᾶς αὐτὰν ταὐτὸν
γε γ' γέ γὰρ γάρ δαῖτα δαιτὸς δαιτὶ δαὶ δαιτί δαῖτ' δαΐδας δαΐδων δἰ διὰ διά δὲ δ' δέ δὴ δή εἰ εἴ κεἰ κεἴ αἴ αἲ εἲ αἰ
ἐστί ἐστιν ὢν ἦν ἐστὶν ὦσιν εἶναι ὄντι εἰσιν ἐστι ὄντα οὖσαν ἦσαν ἔστι ὄντας ἐστὲ εἰσὶ εἶ ὤν ἦ οὖσαι ἔσται ἐσμὲν ἐστ' ἐστίν ἔστ' ὦ ἔσει ἦμεν εἰμι εἰσὶν ἦσθ'
ἐστὶ ᾖ οὖσ' ἔστιν εἰμὶ εἴμ' ἐσθ' ᾖς στί εἴην εἶναί οὖσα κἄστ' εἴη ἦσθα εἰμ' ἔστω ὄντ' ἔσθ' ἔμμεναι ἔω ἐὼν ἐσσι ἔσσεται ἐστὸν ἔσαν ἔστων ἐόντα ἦεν ἐοῦσαν ἔην
ἔσσομαι εἰσί ἐστόν ἔσκεν ἐόντ' ἐών ἔσσεσθ' εἰσ' ἐόντες ἐόντε ἐσσεῖται εἰμεν ἔασιν ἔσκε ἔμεναι ἔσεσθαι ἔῃ εἰμὲν εἰσι ἐόντας ἔστε εἰς ἦτε εἰμί ἔσσεαι ἔμμεν
ἐοῦσα ἔμεν ᾖσιν ἐστε ἐόντι εἶεν ἔσσονται ἔησθα ἔσεσθε ἐσσί ἐοῦσ' ἔασι ἔα ἦα ἐόν ἔσσεσθαι ἔσομαι ἔσκον εἴης ἔωσιν εἴησαν ἐὸν ἐουσέων ἔσσῃ ἐούσης ἔσονται
ἐούσας ἐόντων ἐόντος ἐσομένην ἔστωσαν ἔωσι ἔας ἐοῦσαι ἣν εἰσίν ἤστην ὄντες ὄντων οὔσας οὔσαις ὄντος οὖσι οὔσης ἔσῃ ὂν ἐσμεν ἐσμέν οὖσιν ἐσομένους ἐσσόμεσθα
ἒς ἐς ἔς ἐν κεἰς εἲς κἀν ἔν κατὰ κατ' καθ' κατά κάτα κὰπ κὰκ κὰδ κὰρ κάρ κὰγ κὰμ καὶ καί μετὰ μεθ' μετ' μέτα μετά μέθ' μέτ' μὲν μέν μὴ
μή μη οὐκ οὒ οὐ οὐχ οὐχὶ κοὐ κοὐχ οὔ κοὐκ οὐχί οὐκὶ οὐδὲν οὐδεὶς οὐδέν κοὐδεὶς κοὐδὲν οὐδένα οὐδενὸς οὐδέν' οὐδενός οὐδενὶ
οὐδεμία οὐδείς οὐδεμίαν οὐδὲ οὐδ' κοὐδ' οὐδέ οὔτε οὔθ' οὔτέ τε οὔτ' οὕτως οὕτω οὕτῶ χοὔτως οὖν ὦν ὧν τοῦτο τοῦθ' τοῦτον τούτῳ
τούτοις ταύτας αὕτη ταῦτα οὗτος ταύτης ταύτην τούτων ταῦτ' τοῦτ' τούτου αὗται τούτους τοῦτό ταῦτά τούτοισι χαὔτη ταῦθ' χοὖτοι
τούτοισιν οὗτός οὗτοι τούτω τουτέων τοῦτὸν οὗτοί τοῦτου οὗτοὶ ταύτῃσι ταύταις ταυτὶ παρὰ παρ' πάρα παρά πὰρ παραὶ πάρ' περὶ
πέρι περί πρὸς πρός ποτ' ποτὶ προτὶ προτί πότι
σὸς σήν σὴν σὸν σόν σὰ σῶν σοῖσιν σός σῆς σῷ σαῖς σῇ σοῖς σοῦ σ' σὰν σά σὴ σὰς
σᾷ σοὺς σούς σοῖσι σῇς σῇσι σή σῇσιν σοὶ σου ὑμεῖς σὲ σύ σοι ὑμᾶς ὑμῶν ὑμῖν σε
σέ σὺ σέθεν σοί ὑμὶν σφῷν ὑμίν τοι τοὶ σφὼ ὔμμ' σφῶϊ σεῖο τ' σφῶϊν ὔμμιν σέο σευ σεῦ
ὔμμι ὑμέων τύνη ὑμείων τοί ὔμμες σεο τέ τεοῖο ὑμέας σὺν ξὺν σύν
θ' τί τι τις τινες τινα τινος τινὸς τινὶ τινῶν τίς τίνες τινὰς τιν' τῳ του τίνα τοῦ τῷ τινί τινά τίνος τινι τινας τινὰ τινων
τίν' τευ τέο τινές τεο τινὲς τεῷ τέῳ τινός τεῳ τισὶ
τοιαῦτα τοιοῦτον τοιοῦθ' τοιοῦτος τοιαύτην τοιαῦτ' τοιούτου τοιαῦθ' τοιαύτῃ τοιούτοις τοιαῦται τοιαῦτά τοιαύτη τοιοῦτοι τοιούτων τοιούτοισι
τοιοῦτο τοιούτους τοιούτῳ τοιαύτης τοιαύταις τοιαύτας τοιοῦτός τίνι τοῖσι τίνων τέων τέοισί τὰ τῇ τώ τὼ
ἀλλὰ ἀλλ' ἀλλά ἀπ' ἀπὸ κἀπ' ἀφ' τἀπὸ κἀφ' ἄπο ἀπό τὠπὸ τἀπ' ἄλλων ἄλλῳ ἄλλη ἄλλης ἄλλους ἄλλοις ἄλλον ἄλλο ἄλλου τἄλλα ἄλλα
ἄλλᾳ ἄλλοισιν τἄλλ' ἄλλ' ἄλλος ἄλλοισι κἄλλ' ἄλλοι ἄλλῃσι ἄλλόν ἄλλην ἄλλά ἄλλαι ἄλλοισίν ὧλλοι ἄλλῃ ἄλλας ἀλλέων τἆλλα ἄλλως
ἀλλάων ἄλλαις τἆλλ'
ἂν ἄν κἂν τἂν ἃν κεν κ' κέν κέ κε χ' ἄρα τἄρα ἄρ' τἄρ' ἄρ ῥα ῥά ῥ τὰρ ἄρά ἂρ
ἡμᾶς με ἐγὼ ἐμὲ μοι κἀγὼ ἡμῶν ἡμεῖς ἐμοὶ ἔγωγ' ἁμοὶ ἡμῖν μ' ἔγωγέ ἐγώ ἐμοί ἐμοῦ κἀμοῦ ἔμ' κἀμὲ ἡμὶν μου ἐμέ ἔγωγε νῷν νὼ χἠμεῖς ἁμὲ κἀγώ κἀμοὶ χἠμᾶς
ἁγὼ ἡμίν κἄμ' ἔμοιγ' μοί τοὐμὲ ἄμμε ἐγὼν ἐμεῦ ἐμεῖο μευ ἔμοιγε ἄμμι μέ ἡμέας νῶϊ ἄμμιν ἧμιν ἐγών νῶΐ ἐμέθεν ἥμιν ἄμμες νῶι ἡμείων ἄμμ' ἡμέων ἐμέο
ἐκ ἔκ ἐξ κἀκ κ ἃκ κἀξ ἔξ εξ Ἐκ τἀμὰ ἐμοῖς τοὐμόν ἐμᾶς τοὐμὸν ἐμῶν ἐμὸς ἐμῆς ἐμῷ τὠμῷ ἐμὸν τἄμ' ἐμὴ ἐμὰς ἐμαῖς ἐμὴν ἐμόν ἐμὰ ἐμός ἐμοὺς ἐμῇ ἐμᾷ
οὑμὸς ἐμοῖν οὑμός κἀμὸν ἐμαὶ ἐμή ἐμάς ἐμοῖσι ἐμοῖσιν ἐμῇσιν ἐμῇσι ἐμῇς ἐμήν
ἔνι ἐνὶ εἰνὶ εἰν ἐμ ἐπὶ ἐπ' ἔπι ἐφ' κἀπὶ τἀπὶ ἐπί ἔφ' ἔπ' ἐὰν ἢν ἐάν ἤν ἄνπερ
αὑτοῖς αὑτὸν αὑτῷ ἑαυτοῦ αὑτόν αὑτῆς αὑτῶν αὑτοῦ αὑτὴν αὑτοῖν χαὐτοῦ αὑταῖς ἑωυτοῦ ἑωυτῇ ἑωυτὸν ἐωυτῷ ἑωυτῆς ἑωυτόν ἑωυτῷ
ἑωυτάς ἑωυτῶν ἑωυτοὺς ἑωυτοῖσι ἑαυτῇ ἑαυτούς αὑτοὺς ἑαυτῶν ἑαυτοὺς ἑαυτὸν ἑαυτῷ ἑαυτοῖς ἑαυτὴν ἑαυτῆς
ἔτι ἔτ' ἔθ' κἄτι ἢ ἤ ἠέ ἠὲ ἦε ἦέ ἡ τοὺς τὴν τὸ τῶν τὸν ὁ ἁ οἱ τοῖς ταῖς τῆς τὰς αἱ τό τὰν τᾶς τοῖσιν αἳ χὠ τήν τά τοῖν τάς ὅ
χοἰ ἣ ἥ χἠ τάν τᾶν ὃ οἳ οἵ τοῖο τόν τοῖιν τούς τάων ταὶ τῇς τῇσι τῇσιν αἵ τοῖό τοῖσίν ὅττί ταί Τὴν τῆ τῶ τάδε ὅδε τοῦδε τόδε τόνδ'
τάδ' τῆσδε τῷδε ὅδ' τῶνδ' τῇδ' τοῦδέ τῶνδε τόνδε τόδ' τοῦδ' τάσδε τήνδε τάσδ' τήνδ' ταῖσδέ τῇδε τῆσδ' τάνδ' τῷδ' τάνδε ἅδε τοῖσδ' ἥδ'
τᾷδέ τοῖσδε τούσδ' ἥδε τούσδε τώδ' ἅδ' οἵδ' τῶνδέ οἵδε τᾷδε τοῖσδεσσι τώδε τῇδέ τοῖσιδε αἵδε τοῦδὲ τῆδ' αἵδ' τοῖσδεσι ὃν ἃ ὃς ᾧ οὗ ἅπερ
οὓς ἧς οἷς ἅσπερ ᾗ ἅ χὦνπερ ὣ αἷς ᾇ ὅς ἥπερ ἃς ὅσπερ ὅνπερ ὧνπερ ᾧπερ ὅν αἷν οἷσι ἇς ἅς ὥ οὕς ἥν οἷσιν ἕης ὅου ᾗς οἷσί οἷσίν τοῖσί ᾗσιν οἵπερ αἷσπερ
ὅστις ἥτις ὅτου ὅτοισι ἥντιν' ὅτῳ ὅντιν' ὅττι ἅσσά ὅτεῳ ὅτις ὅτιν' ὅτευ ἥντινα αἵτινές ὅντινα ἅσσα ᾧτινι οἵτινες ὅτι ἅτις ὅτ' ὑμὴ
ὑμήν ὑμὸν ὑπὲρ ὕπερ ὑπέρτερον ὑπεὶρ ὑπέρτατος ὑπὸ ὑπ' ὑφ' ὕπο ὑπαὶ ὑπό ὕπ' ὕφ'
ὣς ὡς ὥς ὧς ὥστ' ὥστε ὥσθ' ὤ ὢ
""".split()
)
| 4,757 | 75.741935 | 157 | py |
spaCy | spaCy-master/spacy/lang/grc/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for token in ["᾽Απ'", "᾽ΑΠ'", "ἀφ'", "᾽Αφ", "ἀπὸ"]:
_exc[token] = [{ORTH: token, NORM: "από"}]
for token in ["᾽Αλλ'", "ἀλλ'", "ἀλλὰ"]:
_exc[token] = [{ORTH: token, NORM: "ἀλλά"}]
for token in ["παρ'", "Παρ'", "παρὰ", "παρ"]:
_exc[token] = [{ORTH: token, NORM: "παρά"}]
for token in ["καθ'", "Καθ'", "κατ'", "Κατ'", "κατὰ"]:
_exc[token] = [{ORTH: token, NORM: "κατά"}]
for token in ["Ἐπ'", "ἐπ'", "ἐπὶ", "Εφ'", "εφ'"]:
_exc[token] = [{ORTH: token, NORM: "επί"}]
for token in ["Δι'", "δι'", "διὰ"]:
_exc[token] = [{ORTH: token, NORM: "διά"}]
for token in ["Ὑπ'", "ὑπ'", "ὑφ'"]:
_exc[token] = [{ORTH: token, NORM: "ὑπό"}]
for token in ["Μετ'", "μετ'", "μεθ'", "μετὰ"]:
_exc[token] = [{ORTH: token, NORM: "μετά"}]
for token in ["Μ'", "μ'", "μέ", "μὲ"]:
_exc[token] = [{ORTH: token, NORM: "με"}]
for token in ["Σ'", "σ'", "σέ", "σὲ"]:
_exc[token] = [{ORTH: token, NORM: "σε"}]
for token in ["Τ'", "τ'", "τέ", "τὲ"]:
_exc[token] = [{ORTH: token, NORM: "τε"}]
for token in ["Δ'", "δ'", "δὲ"]:
_exc[token] = [{ORTH: token, NORM: "δέ"}]
_other_exc = {
"μὲν": [{ORTH: "μὲν", NORM: "μέν"}],
"μὴν": [{ORTH: "μὴν", NORM: "μήν"}],
"τὴν": [{ORTH: "τὴν", NORM: "τήν"}],
"τὸν": [{ORTH: "τὸν", NORM: "τόν"}],
"καὶ": [{ORTH: "καὶ", NORM: "καί"}],
"καὐτός": [{ORTH: "κ", NORM: "καί"}, {ORTH: "αὐτός"}],
"καὐτὸς": [{ORTH: "κ", NORM: "καί"}, {ORTH: "αὐτὸς", NORM: "αὐτός"}],
"κοὐ": [{ORTH: "κ", NORM: "καί"}, {ORTH: "οὐ"}],
"χἡ": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ἡ"}],
"χοἱ": [{ORTH: "χ", NORM: "καί"}, {ORTH: "οἱ"}],
"χἱκετεύετε": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ἱκετεύετε"}],
"κἀν": [{ORTH: "κ", NORM: "καί"}, {ORTH: "ἀν", NORM: "ἐν"}],
"κἀγὼ": [{ORTH: "κἀ", NORM: "καί"}, {ORTH: "γὼ", NORM: "ἐγώ"}],
"κἀγώ": [{ORTH: "κἀ", NORM: "καί"}, {ORTH: "γώ", NORM: "ἐγώ"}],
"ἁγώ": [{ORTH: "ἁ", NORM: "ἃ"}, {ORTH: "γώ", NORM: "ἐγώ"}],
"ἁγὼ": [{ORTH: "ἁ", NORM: "ἃ"}, {ORTH: "γὼ", NORM: "ἐγώ"}],
"ἐγᾦδα": [{ORTH: "ἐγ", NORM: "ἐγώ"}, {ORTH: "ᾦδα", NORM: "οἶδα"}],
"ἐγᾦμαι": [{ORTH: "ἐγ", NORM: "ἐγώ"}, {ORTH: "ᾦμαι", NORM: "οἶμαι"}],
"κἀς": [{ORTH: "κ", NORM: "καί"}, {ORTH: "ἀς", NORM: "ἐς"}],
"κᾆτα": [{ORTH: "κ", NORM: "καί"}, {ORTH: "ᾆτα", NORM: "εἶτα"}],
"κεἰ": [{ORTH: "κ", NORM: "καί"}, {ORTH: "εἰ"}],
"κεἰς": [{ORTH: "κ", NORM: "καί"}, {ORTH: "εἰς"}],
"χὤτε": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ὤτε", NORM: "ὅτε"}],
"χὤπως": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ὤπως", NORM: "ὅπως"}],
"χὤτι": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ὤτι", NORM: "ὅτι"}],
"χὤταν": [{ORTH: "χ", NORM: "καί"}, {ORTH: "ὤταν", NORM: "ὅταν"}],
"οὑμός": [{ORTH: "οὑ", NORM: "ὁ"}, {ORTH: "μός", NORM: "ἐμός"}],
"οὑμὸς": [{ORTH: "οὑ", NORM: "ὁ"}, {ORTH: "μὸς", NORM: "ἐμός"}],
"οὑμοί": [{ORTH: "οὑ", NORM: "οἱ"}, {ORTH: "μοί", NORM: "ἐμoί"}],
"οὑμοὶ": [{ORTH: "οὑ", NORM: "οἱ"}, {ORTH: "μοὶ", NORM: "ἐμoί"}],
"σοὔστι": [{ORTH: "σοὔ", NORM: "σοί"}, {ORTH: "στι", NORM: "ἐστι"}],
"σοὐστί": [{ORTH: "σοὐ", NORM: "σοί"}, {ORTH: "στί", NORM: "ἐστί"}],
"σοὐστὶ": [{ORTH: "σοὐ", NORM: "σοί"}, {ORTH: "στὶ", NORM: "ἐστί"}],
"μοὖστι": [{ORTH: "μοὖ", NORM: "μοί"}, {ORTH: "στι", NORM: "ἐστι"}],
"μοὔστι": [{ORTH: "μοὔ", NORM: "μοί"}, {ORTH: "στι", NORM: "ἐστι"}],
"τοὔνομα": [{ORTH: "τοὔ", NORM: "τό"}, {ORTH: "νομα", NORM: "ὄνομα"}],
"οὑν": [{ORTH: "οὑ", NORM: "ὁ"}, {ORTH: "ν", NORM: "ἐν"}],
"ὦνερ": [{ORTH: "ὦ", NORM: "ὦ"}, {ORTH: "νερ", NORM: "ἄνερ"}],
"ὦνδρες": [{ORTH: "ὦ", NORM: "ὦ"}, {ORTH: "νδρες", NORM: "ἄνδρες"}],
"προὔχων": [{ORTH: "προὔ", NORM: "πρό"}, {ORTH: "χων", NORM: "ἔχων"}],
"προὔχοντα": [{ORTH: "προὔ", NORM: "πρό"}, {ORTH: "χοντα", NORM: "ἔχοντα"}],
"ὥνεκα": [{ORTH: "ὥ", NORM: "οὗ"}, {ORTH: "νεκα", NORM: "ἕνεκα"}],
"θοἰμάτιον": [{ORTH: "θο", NORM: "τό"}, {ORTH: "ἰμάτιον"}],
"ὥνεκα": [{ORTH: "ὥ", NORM: "οὗ"}, {ORTH: "νεκα", NORM: "ἕνεκα"}],
"τὠληθές": [{ORTH: "τὠ", NORM: "τὸ"}, {ORTH: "ληθές", NORM: "ἀληθές"}],
"θἡμέρᾳ": [{ORTH: "θ", NORM: "τῇ"}, {ORTH: "ἡμέρᾳ"}],
"ἅνθρωπος": [{ORTH: "ἅ", NORM: "ὁ"}, {ORTH: "νθρωπος", NORM: "ἄνθρωπος"}],
"τἄλλα": [{ORTH: "τ", NORM: "τὰ"}, {ORTH: "ἄλλα"}],
"τἆλλα": [{ORTH: "τἆ", NORM: "τὰ"}, {ORTH: "λλα", NORM: "ἄλλα"}],
"ἁνήρ": [{ORTH: "ἁ", NORM: "ὁ"}, {ORTH: "νήρ", NORM: "ἀνήρ"}],
"ἁνὴρ": [{ORTH: "ἁ", NORM: "ὁ"}, {ORTH: "νὴρ", NORM: "ἀνήρ"}],
"ἅνδρες": [{ORTH: "ἅ", NORM: "οἱ"}, {ORTH: "νδρες", NORM: "ἄνδρες"}],
"ἁγαθαί": [{ORTH: "ἁ", NORM: "αἱ"}, {ORTH: "γαθαί", NORM: "ἀγαθαί"}],
"ἁγαθαὶ": [{ORTH: "ἁ", NORM: "αἱ"}, {ORTH: "γαθαὶ", NORM: "ἀγαθαί"}],
"ἁλήθεια": [{ORTH: "ἁ", NORM: "ἡ"}, {ORTH: "λήθεια", NORM: "ἀλήθεια"}],
"τἀνδρός": [{ORTH: "τ", NORM: "τοῦ"}, {ORTH: "ἀνδρός"}],
"τἀνδρὸς": [{ORTH: "τ", NORM: "τοῦ"}, {ORTH: "ἀνδρὸς", NORM: "ἀνδρός"}],
"τἀνδρί": [{ORTH: "τ", NORM: "τῷ"}, {ORTH: "ἀνδρί"}],
"τἀνδρὶ": [{ORTH: "τ", NORM: "τῷ"}, {ORTH: "ἀνδρὶ", NORM: "ἀνδρί"}],
"αὑτός": [{ORTH: "αὑ", NORM: "ὁ"}, {ORTH: "τός", NORM: "αὐτός"}],
"αὑτὸς": [{ORTH: "αὑ", NORM: "ὁ"}, {ORTH: "τὸς", NORM: "αὐτός"}],
"ταὐτοῦ": [{ORTH: "τ", NORM: "τοῦ"}, {ORTH: "αὐτοῦ"}],
}
_exc.update(_other_exc)
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 5,395 | 47.178571 | 80 | py |
spaCy | spaCy-master/spacy/lang/gu/__init__.py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class GujaratiDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Gujarati(Language):
lang = "gu"
Defaults = GujaratiDefaults
__all__ = ["Gujarati"]
| 251 | 15.8 | 46 | py |
spaCy | spaCy-master/spacy/lang/gu/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.gu.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"લોકશાહી એ સરકારનું એક એવું તંત્ર છે જ્યાં નાગરિકો મત દ્વારા સત્તાનો ઉપયોગ કરે છે.",
"તે ગુજરાત રાજ્યના ધરમપુર શહેરમાં આવેલું હતું",
"કર્ણદેવ પહેલો સોલંકી વંશનો રાજા હતો",
"તેજપાળને બે પત્ની હતી",
"ગુજરાતમાં ભારતીય જનતા પક્ષનો ઉદય આ સમયગાળા દરમિયાન થયો",
"આંદોલનકારીઓએ ચીમનભાઇ પટેલના રાજીનામાની માંગણી કરી.",
"અહિયાં શું જોડાય છે?",
"મંદિરનો પૂર્વાભિમુખ ભાગ નાના મંડપ સાથે થોડો લંબચોરસ આકારનો છે.",
]
| 595 | 30.368421 | 88 | py |
spaCy | spaCy-master/spacy/lang/gu/stop_words.py | STOP_WORDS = set(
"""
એમ
આ
એ
રહી
છે
છો
હતા
હતું
હતી
હોય
હતો
શકે
તે
તેના
તેનું
તેને
તેની
તેઓ
તેમને
તેમના
તેમણે
તેમનું
તેમાં
અને
અહીં
થી
થઈ
થાય
જે
ને
કે
ના
ની
નો
ને
નું
શું
માં
પણ
પર
જેવા
જેવું
જાય
જેમ
જેથી
માત્ર
માટે
પરથી
આવ્યું
એવી
આવી
રીતે
સુધી
થાય
થઈ
સાથે
લાગે
હોવા
છતાં
રહેલા
કરી
કરે
કેટલા
કોઈ
કેમ
કર્યો
કર્યુ
કરે
સૌથી
ત્યારબાદ
તથા
દ્વારા
જુઓ
જાઓ
જ્યારે
ત્યારે
શકો
નથી
હવે
અથવા
થતો
દર
એટલો
પરંતુ
""".split()
)
| 418 | 3.707865 | 17 | py |
spaCy | spaCy-master/spacy/lang/he/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class HebrewDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
writing_system = {"direction": "rtl", "has_case": False, "has_letters": True}
class Hebrew(Language):
lang = "he"
Defaults = HebrewDefaults
__all__ = ["Hebrew"]
| 391 | 20.777778 | 81 | py |
spaCy | spaCy-master/spacy/lang/he/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.he.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"סין מקימה קרן של 440 מיליון דולר להשקעה בהייטק בישראל",
'רה"מ הודיע כי יחרים טקס בחסותו',
"הכנסת צפויה לאשר איכון אוטומטי של שיחות למוקד 100",
"תוכנית לאומית תהפוך את ישראל למעצמה דיגיטלית",
"סע לשלום, המפתחות בפנים.",
"מלצר, פעמיים טורקי!",
"ואהבת לרעך כמוך.",
"היום נעשה משהו בלתי נשכח.",
"איפה הילד?",
"מיהו נשיא צרפת?",
"מהי בירת ארצות הברית?",
"איך קוראים בעברית לצ'ופצ'יק של הקומקום?",
"מה הייתה הדקה?",
"מי אומר שלום ראשון, זה שעולה או זה שיורד?",
]
| 676 | 26.08 | 60 | py |
spaCy | spaCy-master/spacy/lang/he/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"אפס",
"אחד",
"אחת",
"שתיים",
"שתים",
"שניים",
"שנים",
"שלוש",
"שלושה",
"ארבע",
"ארבעה",
"חמש",
"חמישה",
"שש",
"שישה",
"שבע",
"שבעה",
"שמונה",
"תשע",
"תשעה",
"עשר",
"עשרה",
"אחד עשר",
"אחת עשרה",
"שנים עשר",
"שתים עשרה",
"שלושה עשר",
"שלוש עשרה",
"ארבעה עשר",
"ארבע עשרה",
"חמישה עשר",
"חמש עשרה",
"ששה עשר",
"שש עשרה",
"שבעה עשר",
"שבע עשרה",
"שמונה עשר",
"שמונה עשרה",
"תשעה עשר",
"תשע עשרה",
"עשרים",
"שלושים",
"ארבעים",
"חמישים",
"שישים",
"שבעים",
"שמונים",
"תשעים",
"מאה",
"אלף",
"מליון",
"מליארד",
"טריליון",
]
_ordinal_words = [
"ראשון",
"שני",
"שלישי",
"רביעי",
"חמישי",
"שישי",
"שביעי",
"שמיני",
"תשיעי",
"עשירי",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
# Check ordinal number
if text in _ordinal_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 1,426 | 13.864583 | 49 | py |
spaCy | spaCy-master/spacy/lang/he/stop_words.py | STOP_WORDS = set(
"""
אני
את
אתה
אנחנו
אתן
אתם
הם
הן
היא
הוא
שלי
שלו
שלך
שלה
שלנו
שלכם
שלכן
שלהם
שלהן
לי
לו
לה
לנו
לכם
לכן
להם
להן
אותה
אותו
זה
זאת
אלה
אלו
תחת
מתחת
מעל
בין
עם
עד
על
אל
מול
של
אצל
כמו
אחר
אותו
בלי
לפני
אחרי
מאחורי
עלי
עליו
עליה
עליך
עלינו
עליכם
עליכן
עליהם
עליהן
כל
כולם
כולן
כך
ככה
כזה
כזאת
זה
אותי
אותה
אותם
אותך
אותו
אותן
אותנו
ואת
את
אתכם
אתכן
איתי
איתו
איתך
איתה
איתם
איתן
איתנו
איתכם
איתכן
יהיה
תהיה
הייתי
היתה
היה
להיות
עצמי
עצמו
עצמה
עצמם
עצמן
עצמנו
מי
מה
איפה
היכן
במקום שבו
אם
לאן
למקום שבו
מקום בו
איזה
מהיכן
איך
כיצד
באיזו מידה
מתי
בשעה ש
כאשר
כש
למרות
לפני
אחרי
מאיזו סיבה
הסיבה שבגללה
למה
מדוע
לאיזו תכלית
כי
יש
אין
אך
מנין
מאין
מאיפה
יכל
יכלה
יכלו
יכול
יכולה
יכולים
יכולות
יוכלו
יוכל
מסוגל
לא
רק
אולי
אין
לאו
אי
כלל
בעד
נגד
אם
עם
אל
אלה
אלו
אף
על
מעל
מתחת
מצד
בשביל
לבין
באמצע
בתוך
דרך
מבעד
באמצעות
למעלה
למטה
מחוץ
מן
לעבר
מכאן
כאן
הנה
הרי
פה
שם
אך
ברם
שוב
אבל
מבלי
בלי
מלבד
רק
בגלל
מכיוון
עד
אשר
ואילו
למרות
כמו
כפי
אז
אחרי
כן
לכן
לפיכך
עז
מאוד
מעט
מעטים
במידה
שוב
יותר
מדי
גם
כן
נו
אחר
אחרת
אחרים
אחרות
אשר
או
""".split()
)
| 1,061 | 3.762332 | 17 | py |
spaCy | spaCy-master/spacy/lang/hi/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class HindiDefaults(BaseDefaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Hindi(Language):
lang = "hi"
Defaults = HindiDefaults
__all__ = ["Hindi"]
| 305 | 17 | 46 | py |
spaCy | spaCy-master/spacy/lang/hi/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hi.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"एप्पल 1 अरब डॉलर के लिए यू.के. स्टार्टअप खरीदने पर विचार कर रहा है।",
"स्वायत्त कारें निर्माताओं की ओर बीमा दायित्व रखतीं हैं।",
"सैन फ्रांसिस्को फुटपाथ वितरण रोबोटों पर प्रतिबंध लगाने का विचार कर रहा है।",
"लंदन यूनाइटेड किंगडम का विशाल शहर है।",
"आप कहाँ हो?",
"फ्रांस के राष्ट्रपति कौन हैं?",
"संयुक्त राज्यों की राजधानी क्या है?",
"बराक ओबामा का जन्म कब हुआ था?",
"जवाहरलाल नेहरू भारत के पहले प्रधानमंत्री हैं।",
"राजेंद्र प्रसाद, भारत के पहले राष्ट्रपति, दो कार्यकाल के लिए कार्यालय रखने वाले एकमात्र व्यक्ति हैं।",
]
| 726 | 33.619048 | 107 | py |
spaCy | spaCy-master/spacy/lang/hi/lex_attrs.py | from ...attrs import LIKE_NUM, NORM
from ..norm_exceptions import BASE_NORMS
# fmt: off
_stem_suffixes = [
["ो", "े", "ू", "ु", "ी", "ि", "ा"],
["कर", "ाओ", "िए", "ाई", "ाए", "ने", "नी", "ना", "ते", "ीं", "ती", "ता", "ाँ", "ां", "ों", "ें"],
["ाकर", "ाइए", "ाईं", "ाया", "ेगी", "ेगा", "ोगी", "ोगे", "ाने", "ाना", "ाते", "ाती", "ाता", "तीं", "ाओं", "ाएं", "ुओं", "ुएं", "ुआं"],
["ाएगी", "ाएगा", "ाओगी", "ाओगे", "एंगी", "ेंगी", "एंगे", "ेंगे", "ूंगी", "ूंगा", "ातीं", "नाओं", "नाएं", "ताओं", "ताएं", "ियाँ", "ियों", "ियां"],
["ाएंगी", "ाएंगे", "ाऊंगी", "ाऊंगा", "ाइयाँ", "ाइयों", "ाइयां"]
]
# reference 1: https://en.wikipedia.org/wiki/Indian_numbering_system
# reference 2: https://blogs.transparent.com/hindi/hindi-numbers-1-100/
# reference 3: https://www.mindurhindi.com/basic-words-and-phrases-in-hindi/
_one_to_ten = [
"शून्य",
"एक",
"दो",
"तीन",
"चार",
"पांच", "पाँच",
"छह",
"सात",
"आठ",
"नौ",
"दस",
]
_eleven_to_beyond = [
"ग्यारह",
"बारह",
"तेरह",
"चौदह",
"पंद्रह",
"सोलह",
"सत्रह",
"अठारह",
"उन्नीस",
"बीस",
"इकीस", "इक्कीस",
"बाईस",
"तेइस",
"चौबीस",
"पच्चीस",
"छब्बीस",
"सताइस", "सत्ताइस",
"अट्ठाइस",
"उनतीस",
"तीस",
"इकतीस", "इकत्तीस",
"बतीस", "बत्तीस",
"तैंतीस",
"चौंतीस",
"पैंतीस",
"छतीस", "छत्तीस",
"सैंतीस",
"अड़तीस",
"उनतालीस", "उनत्तीस",
"चालीस",
"इकतालीस",
"बयालीस",
"तैतालीस",
"चवालीस",
"पैंतालीस",
"छयालिस",
"सैंतालीस",
"अड़तालीस",
"उनचास",
"पचास",
"इक्यावन",
"बावन",
"तिरपन", "तिरेपन",
"चौवन", "चउवन",
"पचपन",
"छप्पन",
"सतावन", "सत्तावन",
"अठावन",
"उनसठ",
"साठ",
"इकसठ",
"बासठ",
"तिरसठ", "तिरेसठ",
"चौंसठ",
"पैंसठ",
"छियासठ",
"सड़सठ",
"अड़सठ",
"उनहत्तर",
"सत्तर",
"इकहत्तर",
"बहत्तर",
"तिहत्तर",
"चौहत्तर",
"पचहत्तर",
"छिहत्तर",
"सतहत्तर",
"अठहत्तर",
"उन्नासी", "उन्यासी"
"अस्सी",
"इक्यासी",
"बयासी",
"तिरासी",
"चौरासी",
"पचासी",
"छियासी",
"सतासी",
"अट्ठासी",
"नवासी",
"नब्बे",
"इक्यानवे",
"बानवे",
"तिरानवे",
"चौरानवे",
"पचानवे",
"छियानवे",
"सतानवे",
"अट्ठानवे",
"निन्यानवे",
"सौ",
"हज़ार",
"लाख",
"करोड़",
"अरब",
"खरब",
]
_num_words = _one_to_ten + _eleven_to_beyond
_ordinal_words_one_to_ten = [
"प्रथम", "पहला",
"द्वितीय", "दूसरा",
"तृतीय", "तीसरा",
"चौथा",
"पांचवाँ",
"छठा",
"सातवाँ",
"आठवाँ",
"नौवाँ",
"दसवाँ",
]
_ordinal_suffix = "वाँ"
# fmt: on
def norm(string):
# normalise base exceptions, e.g. punctuation or currency symbols
if string in BASE_NORMS:
return BASE_NORMS[string]
# set stem word as norm, if available, adapted from:
# http://computing.open.ac.uk/Sites/EACLSouthAsia/Papers/p6-Ramanathan.pdf
# http://research.variancia.com/hindi_stemmer/
# https://github.com/taranjeet/hindi-tokenizer/blob/master/HindiTokenizer.py#L142
for suffix_group in reversed(_stem_suffixes):
length = len(suffix_group[0])
if len(string) <= length:
continue
for suffix in suffix_group:
if string.endswith(suffix):
return string[:-length]
return string
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
return True
# check ordinal numbers
# reference: http://www.englishkitab.com/Vocabulary/Numbers.html
if text in _ordinal_words_one_to_ten:
return True
if text.endswith(_ordinal_suffix):
if text[: -len(_ordinal_suffix)] in _eleven_to_beyond:
return True
return False
LEX_ATTRS = {NORM: norm, LIKE_NUM: like_num}
| 4,095 | 20.671958 | 149 | py |
spaCy | spaCy-master/spacy/lang/hi/stop_words.py | # Source: https://github.com/taranjeet/hindi-tokenizer/blob/master/stopwords.txt, https://data.mendeley.com/datasets/bsr3frvvjc/1#file-a21d5092-99d7-45d8-b044-3ae9edd391c6
STOP_WORDS = set(
"""
अंदर
अत
अदि
अप
अपना
अपनि
अपनी
अपने
अभि
अभी
अंदर
आदि
आप
अगर
इंहिं
इंहें
इंहों
इतयादि
इत्यादि
इन
इनका
इन्हीं
इन्हें
इन्हों
इस
इसका
इसकि
इसकी
इसके
इसमें
इसि
इसी
इसे
उंहिं
उंहें
उंहों
उन
उनका
उनकि
उनकी
उनके
उनको
उन्हीं
उन्हें
उन्हों
उस
उसके
उसि
उसी
उसे
एक
एवं
एस
एसे
ऐसे
ओर
और
कइ
कई
कर
करता
करते
करना
करने
करें
कहते
कहा
का
काफि
काफ़ी
कि
किंहें
किंहों
कितना
किन्हें
किन्हों
किया
किर
किस
किसि
किसी
किसे
की
कुछ
कुल
के
को
कोइ
कोई
कोन
कोनसा
कौन
कौनसा
गया
घर
जब
जहाँ
जहां
जा
जिंहें
जिंहों
जितना
जिधर
जिन
जिन्हें
जिन्हों
जिस
जिसे
जीधर
जेसा
जेसे
जैसा
जैसे
जो
तक
तब
तरह
तिंहें
तिंहों
तिन
तिन्हें
तिन्हों
तिस
तिसे
तो
था
थि
थी
थे
दबारा
दवारा
दिया
दुसरा
दुसरे
दूसरे
दो
द्वारा
न
नहिं
नहीं
ना
निचे
निहायत
नीचे
ने
पर
पहले
पुरा
पूरा
पे
फिर
बनि
बनी
बहि
बही
बहुत
बाद
बाला
बिलकुल
भि
भितर
भी
भीतर
मगर
मानो
मे
में
मैं
मुझको
मेरा
यदि
यह
यहाँ
यहां
यहि
यही
या
यिह
ये
रखें
रवासा
रहा
रहे
ऱ्वासा
लिए
लिये
लेकिन
व
वगेरह
वग़ैरह
वरग
वर्ग
वह
वहाँ
वहां
वहिं
वहीं
वाले
वुह
वे
वग़ैरह
संग
सकता
सकते
सबसे
सभि
सभी
साथ
साबुत
साभ
सारा
से
सो
संग
हि
ही
हुअ
हुआ
हुइ
हुई
हुए
हे
हें
है
हैं
हो
हूँ
होता
होति
होती
होते
होना
होने
""".split()
)
| 1,289 | 4.375 | 171 | py |
spaCy | spaCy-master/spacy/lang/hr/__init__.py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class CroatianDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Croatian(Language):
lang = "hr"
Defaults = CroatianDefaults
__all__ = ["Croatian"]
| 251 | 15.8 | 46 | py |
spaCy | spaCy-master/spacy/lang/hr/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ovo je rečenica.",
"Kako se popravlja auto?",
"Zagreb je udaljen od Ljubljane svega 150 km.",
"Nećete vjerovati što se dogodilo na ovogodišnjem festivalu!",
"Budućnost Apple je upitna nakon dugotrajnog pada vrijednosti dionica firme.",
"Trgovina oružjem predstavlja prijetnju za globalni mir.",
]
| 483 | 29.25 | 82 | py |
spaCy | spaCy-master/spacy/lang/hr/stop_words.py | # Source: https://github.com/stopwords-iso/stopwords-hr
STOP_WORDS = set(
"""
a
ah
aha
aj
ako
al
ali
arh
au
avaj
bar
baš
bez
bi
bih
bijah
bijahu
bijaše
bijasmo
bijaste
bila
bili
bilo
bio
bismo
biste
biti
brr
buć
budavši
bude
budimo
budite
budu
budući
bum
bumo
će
ćemo
ćeš
ćete
čijem
čijim
čijima
ću
da
daj
dakle
de
deder
dem
djelomice
djelomično
do
doista
dok
dokle
donekle
dosad
doskoro
dotad
dotle
dovečer
drugamo
drugdje
duž
e
eh
ehe
ej
eno
eto
evo
ga
gdjekakav
gdjekoje
gic
god
halo
hej
hm
hoće
hoćemo
hoćeš
hoćete
hoću
hop
htijahu
htijasmo
htijaste
htio
htjedoh
htjedoše
htjedoste
htjela
htjele
htjeli
hura
i
iako
ih
iju
ijuju
ikada
ikakav
ikakva
ikakve
ikakvi
ikakvih
ikakvim
ikakvima
ikakvo
ikakvog
ikakvoga
ikakvoj
ikakvom
ikakvome
ili
im
iz
ja
je
jedna
jedne
jedni
jedno
jer
jesam
jesi
jesmo
jest
jeste
jesu
jim
joj
još
ju
kada
kako
kao
koja
koje
koji
kojima
koju
kroz
lani
li
me
mene
meni
mi
mimo
moj
moja
moje
moji
moju
mu
na
nad
nakon
nam
nama
nas
naš
naša
naše
našeg
naši
ne
neće
nećemo
nećeš
nećete
neću
nego
neka
neke
neki
nekog
neku
nema
nešto
netko
ni
nije
nikoga
nikoje
nikoji
nikoju
nisam
nisi
nismo
niste
nisu
njega
njegov
njegova
njegovo
njemu
njezin
njezina
njezino
njih
njihov
njihova
njihovo
njim
njima
njoj
nju
no
o
od
odmah
on
ona
one
oni
ono
onu
onoj
onom
onim
onima
ova
ovaj
ovim
ovima
ovoj
pa
pak
pljus
po
pod
podalje
poimence
poizdalje
ponekad
pored
postrance
potajice
potrbuške
pouzdano
prije
s
sa
sam
samo
sasvim
sav
se
sebe
sebi
si
šic
smo
ste
što
šta
štogod
štagod
su
sva
sve
svi
svi
svog
svoj
svoja
svoje
svoju
svom
svu
ta
tada
taj
tako
te
tebe
tebi
ti
tim
tima
to
toj
tome
tu
tvoj
tvoja
tvoje
tvoji
tvoju
u
usprkos
utaman
uvijek
uz
uza
uzagrapce
uzalud
uzduž
valjda
vam
vama
vas
vaš
vaša
vaše
vašim
vašima
već
vi
vjerojatno
vjerovatno
vrh
vrlo
za
zaista
zar
zatim
zato
zbija
zbog
želeći
željah
željela
željele
željeli
željelo
željen
željena
željene
željeni
željenu
željeo
zimus
zum
""".split()
)
| 1,936 | 4.614493 | 55 | py |
spaCy | spaCy-master/spacy/lang/hsb/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class UpperSorbianDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
class UpperSorbian(Language):
lang = "hsb"
Defaults = UpperSorbianDefaults
__all__ = ["UpperSorbian"]
| 437 | 22.052632 | 54 | py |
spaCy | spaCy-master/spacy/lang/hsb/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hsb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"To běšo wjelgin raźone a jo se wót luźi derje pśiwzeło. Tak som dožywiła wjelgin",
"Jogo pśewóźowarce stej groniłej, až how w serbskich stronach njama Santa Claus nic pytaś.",
"A ten sobuźěłaśeŕ Statneje biblioteki w Barlinju jo pśimjeł drogotne knigły bźez rukajcowu z nagima rukoma!",
"Take wobchadanje z našym kulturnym derbstwom zewšym njejźo.",
"Wopśimjeśe drugich pśinoskow jo było na wusokem niwowje, ako pśecej.",
]
| 608 | 37.0625 | 114 | py |
spaCy | spaCy-master/spacy/lang/hsb/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"nul",
"jedyn",
"jedna",
"jedne",
"dwaj",
"dwě",
"tři",
"třo",
"štyri",
"štyrjo",
"pjeć",
"šěsć",
"sydom",
"wosom",
"dźewjeć",
"dźesać",
"jědnaće",
"dwanaće",
"třinaće",
"štyrnaće",
"pjatnaće",
"šěsnaće",
"sydomnaće",
"wosomnaće",
"dźewjatnaće",
"dwaceći",
"třiceći",
"štyrceći",
"pjećdźesat",
"šěsćdźesat",
"sydomdźesat",
"wosomdźesat",
"dźewjećdźesat",
"sto",
"tysac",
"milion",
"miliarda",
"bilion",
"biliarda",
"trilion",
"triliarda",
]
_ordinal_words = [
"prěni",
"prěnja",
"prěnje",
"druhi",
"druha",
"druhe",
"třeći",
"třeća",
"třeće",
"štwórty",
"štwórta",
"štwórte",
"pjaty",
"pjata",
"pjate",
"šěsty",
"šěsta",
"šěste",
"sydmy",
"sydma",
"sydme",
"wosmy",
"wosma",
"wosme",
"dźewjaty",
"dźewjata",
"dźewjate",
"dźesaty",
"dźesata",
"dźesate",
"jědnaty",
"jědnata",
"jědnate",
"dwanaty",
"dwanata",
"dwanate",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
text_lower = text.lower()
if text_lower in _num_words:
return True
# Check ordinal number
if text_lower in _ordinal_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 1,716 | 15.046729 | 49 | py |
spaCy | spaCy-master/spacy/lang/hsb/stop_words.py | STOP_WORDS = set(
"""
a abo ale ani
dokelž
hdyž
jeli jelizo
kaž
pak potom
tež tohodla
zo zoby
""".split()
)
| 119 | 5 | 17 | py |
spaCy | spaCy-master/spacy/lang/hsb/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = dict()
for exc_data in [
{ORTH: "mil.", NORM: "milion"},
{ORTH: "wob.", NORM: "wobydler"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"resp.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 386 | 19.368421 | 56 | py |
spaCy | spaCy-master/spacy/lang/hu/__init__.py | from ...language import BaseDefaults, Language
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKEN_MATCH, TOKENIZER_EXCEPTIONS
class HungarianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
suffixes = TOKENIZER_SUFFIXES
infixes = TOKENIZER_INFIXES
token_match = TOKEN_MATCH
stop_words = STOP_WORDS
class Hungarian(Language):
lang = "hu"
Defaults = HungarianDefaults
__all__ = ["Hungarian"]
| 584 | 25.590909 | 82 | py |
spaCy | spaCy-master/spacy/lang/hu/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hu.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Az Apple egy brit startup vásárlását tervezi 1 milliárd dollár értékben.",
"San Francisco vezetése mérlegeli a járdát használó szállító robotok betiltását.",
"London az Egyesült Királyság egy nagy városa.",
]
| 384 | 26.5 | 86 | py |
spaCy | spaCy-master/spacy/lang/hu/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_ICONS,
CONCAT_QUOTES,
LIST_ELLIPSES,
LIST_PUNCT,
LIST_QUOTES,
UNITS,
)
# removing ° from the special icons to keep e.g. 99° as one token
_concat_icons = CONCAT_ICONS.replace("\u00B0", "")
_currency = r"\$¢£€¥฿"
_quotes = CONCAT_QUOTES.replace("'", "")
_units = UNITS.replace("%", "")
_prefixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [_concat_icons]
+ [r"[,.:](?=[{a}])".format(a=ALPHA)]
)
_suffixes = (
[r"\+"]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [_concat_icons]
+ [
r"(?<=[0-9])\+",
r"(?<=°[FfCcKk])\.",
r"(?<=[0-9])(?:[{c}])".format(c=_currency),
r"(?<=[0-9])(?:{u})".format(u=_units),
r"(?<=[{al}{e}{q}(?:{c})])\.".format(
al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, c=_currency
),
r"(?<=[{al})])-e".format(al=ALPHA_LOWER),
]
)
_infixes = (
LIST_ELLIPSES
+ [_concat_icons]
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[:<>=](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])([{q}\)\]\(\[])(?=[\-{a}])".format(a=ALPHA, q=_quotes),
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes
| 1,494 | 23.112903 | 75 | py |
spaCy | spaCy-master/spacy/lang/hu/stop_words.py | STOP_WORDS = set(
"""
a abban ahhoz ahogy ahol aki akik akkor akár alatt amely amelyek amelyekben
amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az
azok azon azonban azt aztán azután azzal azért
be belül benne bár
cikk cikkek cikkeket csak
de
e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen
elo eloször elott elso elég előtt emilyen ennek erre ez ezek ezen ezt ezzel
ezért
fel felé
ha hanem hiszen hogy hogyan hát
ide igen ill ill. illetve ilyen ilyenkor inkább is ismét ison itt
jobban jó jól
kell kellett keressünk keresztül ki kívül között közül
le legalább legyen lehet lehetett lenne lenni lesz lett
ma maga magát majd meg mellett mely melyek mert mi miatt mikor milyen minden
mindenki mindent mindig mint mintha mit mivel miért mondta most már más másik
még míg
nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül
o oda ok oket olyan ott
pedig persze például
rá
s saját sem semmi sok sokat sokkal stb. szemben szerint szinte számára szét
talán te tehát teljes ti tovább továbbá több túl ugyanis
utolsó után utána
vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont
volna volt voltak voltam voltunk
által általában át
én éppen és
így
ön össze
úgy új újabb újra
ő őket
""".split()
)
| 1,309 | 19.793651 | 77 | py |
spaCy | spaCy-master/spacy/lang/hu/tokenizer_exceptions.py | import re
from ...symbols import ORTH
from ...util import update_exc
from ..punctuation import ALPHA_LOWER, CURRENCY
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
for orth in [
"-e",
"A.",
"AG.",
"AkH.",
"Aö.",
"B.",
"B.CS.",
"B.S.",
"B.Sc.",
"B.ú.é.k.",
"BE.",
"BEK.",
"BSC.",
"BSc.",
"BTK.",
"Bat.",
"Be.",
"Bek.",
"Bfok.",
"Bk.",
"Bp.",
"Bros.",
"Bt.",
"Btk.",
"Btke.",
"Btét.",
"C.",
"CSC.",
"Cal.",
"Cg.",
"Cgf.",
"Cgt.",
"Cia.",
"Co.",
"Colo.",
"Comp.",
"Copr.",
"Corp.",
"Cos.",
"Cs.",
"Csc.",
"Csop.",
"Cstv.",
"Ctv.",
"Ctvr.",
"D.",
"DR.",
"Dipl.",
"Dr.",
"Dsz.",
"Dzs.",
"E.",
"EK.",
"EU.",
"F.",
"Fla.",
"Folyt.",
"Fpk.",
"Főszerk.",
"G.",
"GK.",
"GM.",
"Gfv.",
"Gmk.",
"Gr.",
"Group.",
"Gt.",
"Gy.",
"H.",
"HKsz.",
"Hmvh.",
"I.",
"Ifj.",
"Inc.",
"Inform.",
"Int.",
"J.",
"Jr.",
"Jv.",
"K.",
"K.m.f.",
"KB.",
"KER.",
"KFT.",
"KRT.",
"Kb.",
"Ker.",
"Kft.",
"Kg.",
"Kht.",
"Kkt.",
"Kong.",
"Korm.",
"Kr.",
"Kr.e.",
"Kr.u.",
"Krt.",
"L.",
"LB.",
"Llc.",
"Ltd.",
"M.",
"M.A.",
"M.S.",
"M.SC.",
"M.Sc.",
"MA.",
"MH.",
"MSC.",
"MSc.",
"Mass.",
"Max.",
"Mlle.",
"Mme.",
"Mo.",
"Mr.",
"Mrs.",
"Ms.",
"Mt.",
"N.",
"N.N.",
"NB.",
"NBr.",
"Nat.",
"No.",
"Nr.",
"Ny.",
"Nyh.",
"Nyr.",
"Nyrt.",
"O.",
"OJ.",
"Op.",
"P.",
"P.H.",
"P.S.",
"PH.D.",
"PHD.",
"PROF.",
"Pf.",
"Ph.D",
"PhD.",
"Pk.",
"Pl.",
"Plc.",
"Pp.",
"Proc.",
"Prof.",
"Ptk.",
"R.",
"RT.",
"Rer.",
"Rt.",
"S.",
"S.B.",
"SZOLG.",
"Salg.",
"Sch.",
"Spa.",
"St.",
"Sz.",
"SzRt.",
"Szerk.",
"Szfv.",
"Szjt.",
"Szolg.",
"Szt.",
"Sztv.",
"Szvt.",
"Számv.",
"T.",
"TEL.",
"Tel.",
"Ty.",
"Tyr.",
"U.",
"Ui.",
"Ut.",
"V.",
"VB.",
"Vcs.",
"Vhr.",
"Vht.",
"Várm.",
"W.",
"X.",
"X.Y.",
"Y.",
"Z.",
"Zrt.",
"Zs.",
"a.C.",
"ac.",
"adj.",
"adm.",
"ag.",
"agit.",
"alez.",
"alk.",
"all.",
"altbgy.",
"an.",
"ang.",
"arch.",
"at.",
"atc.",
"aug.",
"b.a.",
"b.s.",
"b.sc.",
"bek.",
"belker.",
"berend.",
"biz.",
"bizt.",
"bo.",
"bp.",
"br.",
"bsc.",
"bt.",
"btk.",
"ca.",
"cc.",
"cca.",
"cf.",
"cif.",
"co.",
"corp.",
"cos.",
"cs.",
"csc.",
"csüt.",
"cső.",
"ctv.",
"dbj.",
"dd.",
"ddr.",
"de.",
"dec.",
"dikt.",
"dipl.",
"dj.",
"dk.",
"dl.",
"dny.",
"dolg.",
"dr.",
"du.",
"dzs.",
"ea.",
"ed.",
"eff.",
"egyh.",
"ell.",
"elv.",
"elvt.",
"em.",
"eng.",
"eny.",
"et.",
"etc.",
"ev.",
"ezr.",
"eü.",
"f.h.",
"f.é.",
"fam.",
"fb.",
"febr.",
"fej.",
"felv.",
"felügy.",
"ff.",
"ffi.",
"fhdgy.",
"fil.",
"fiz.",
"fm.",
"foglalk.",
"ford.",
"fp.",
"fr.",
"frsz.",
"fszla.",
"fszt.",
"ft.",
"fuv.",
"főig.",
"főisk.",
"főtörm.",
"főv.",
"gazd.",
"gimn.",
"gk.",
"gkv.",
"gmk.",
"gondn.",
"gr.",
"grav.",
"gy.",
"gyak.",
"gyártm.",
"gör.",
"hads.",
"hallg.",
"hdm.",
"hdp.",
"hds.",
"hg.",
"hiv.",
"hk.",
"hm.",
"ho.",
"honv.",
"hp.",
"hr.",
"hrsz.",
"hsz.",
"ht.",
"htb.",
"hv.",
"hőm.",
"i.e.",
"i.sz.",
"id.",
"ie.",
"ifj.",
"ig.",
"igh.",
"ill.",
"imp.",
"inc.",
"ind.",
"inform.",
"inic.",
"int.",
"io.",
"ip.",
"ir.",
"irod.",
"irod.",
"isk.",
"ism.",
"izr.",
"iá.",
"jan.",
"jav.",
"jegyz.",
"jgmk.",
"jjv.",
"jkv.",
"jogh.",
"jogt.",
"jr.",
"jvb.",
"júl.",
"jún.",
"karb.",
"kat.",
"kath.",
"kb.",
"kcs.",
"kd.",
"ker.",
"kf.",
"kft.",
"kht.",
"kir.",
"kirend.",
"kisip.",
"kiv.",
"kk.",
"kkt.",
"klin.",
"km.",
"korm.",
"kp.",
"krt.",
"kt.",
"ktsg.",
"kult.",
"kv.",
"kve.",
"képv.",
"kísérl.",
"kóth.",
"könyvt.",
"körz.",
"köv.",
"közj.",
"közl.",
"közp.",
"közt.",
"kü.",
"lat.",
"ld.",
"legs.",
"lg.",
"lgv.",
"loc.",
"lt.",
"ltd.",
"ltp.",
"luth.",
"m.a.",
"m.s.",
"m.sc.",
"ma.",
"mat.",
"max.",
"mb.",
"med.",
"megh.",
"met.",
"mf.",
"mfszt.",
"min.",
"miss.",
"mjr.",
"mjv.",
"mk.",
"mlle.",
"mme.",
"mn.",
"mozg.",
"mr.",
"mrs.",
"ms.",
"msc.",
"má.",
"máj.",
"márc.",
"mé.",
"mélt.",
"mü.",
"műh.",
"műsz.",
"műv.",
"művez.",
"nagyker.",
"nagys.",
"nat.",
"nb.",
"neg.",
"nk.",
"no.",
"nov.",
"nu.",
"ny.",
"nyilv.",
"nyrt.",
"nyug.",
"obj.",
"okl.",
"okt.",
"old.",
"olv.",
"orsz.",
"ort.",
"ov.",
"ovh.",
"pf.",
"pg.",
"ph.d",
"ph.d.",
"phd.",
"phil.",
"pjt.",
"pk.",
"pl.",
"plb.",
"plc.",
"pld.",
"plur.",
"pol.",
"polg.",
"poz.",
"pp.",
"proc.",
"prof.",
"prot.",
"pság.",
"ptk.",
"pu.",
"pü.",
"r.k.",
"rac.",
"rad.",
"red.",
"ref.",
"reg.",
"rer.",
"rev.",
"rf.",
"rkp.",
"rkt.",
"rt.",
"rtg.",
"röv.",
"s.b.",
"s.k.",
"sa.",
"sb.",
"sel.",
"sgt.",
"sm.",
"st.",
"stat.",
"stb.",
"strat.",
"stud.",
"sz.",
"szakm.",
"szaksz.",
"szakszerv.",
"szd.",
"szds.",
"szept.",
"szerk.",
"szf.",
"szimf.",
"szjt.",
"szkv.",
"szla.",
"szn.",
"szolg.",
"szt.",
"szubj.",
"szöv.",
"szül.",
"tanm.",
"tb.",
"tbk.",
"tc.",
"techn.",
"tek.",
"tel.",
"tf.",
"tgk.",
"ti.",
"tip.",
"tisztv.",
"titks.",
"tk.",
"tkp.",
"tny.",
"tp.",
"tszf.",
"tszk.",
"tszkv.",
"tv.",
"tvr.",
"ty.",
"törv.",
"tü.",
"ua.",
"ui.",
"unit.",
"uo.",
"uv.",
"vas.",
"vb.",
"vegy.",
"vh.",
"vhol.",
"vhr.",
"vill.",
"vizsg.",
"vk.",
"vkf.",
"vkny.",
"vm.",
"vol.",
"vs.",
"vsz.",
"vv.",
"vál.",
"várm.",
"vízv.",
"vö.",
"zrt.",
"zs.",
"Á.",
"Áe.",
"Áht.",
"É.",
"Épt.",
"Ész.",
"Új-Z.",
"ÚjZ.",
"Ún.",
"á.",
"ált.",
"ápr.",
"ásv.",
"é.",
"ék.",
"ény.",
"érk.",
"évf.",
"í.",
"ó.",
"össz.",
"ötk.",
"özv.",
"ú.",
"ú.n.",
"úm.",
"ún.",
"út.",
"üag.",
"üd.",
"üdv.",
"üe.",
"ümk.",
"ütk.",
"üv.",
"ű.",
"őrgy.",
"őrpk.",
"őrv.",
]:
_exc[orth] = [{ORTH: orth}]
_ord_num_or_date = r"([A-Z0-9]+[./-])*(\d+\.?)"
_num = r"[+\-]?\d+([,.]\d+)*"
_ops = r"[=<>+\-\*/^()÷%²]"
_suffixes = r"-[{al}]+".format(al=ALPHA_LOWER)
_numeric_exp = r"({n})(({o})({n}))*[%]?".format(n=_num, o=_ops)
_time_exp = r"\d+(:\d+)*(\.\d+)?"
_nums = r"(({ne})|({t})|({on})|({c}))({s})?".format(
ne=_numeric_exp, t=_time_exp, on=_ord_num_or_date, c=CURRENCY, s=_suffixes
)
for u in "cfkCFK":
_exc[f"°{u}"] = [{ORTH: f"°{u}"}]
_exc[f"°{u}."] = [{ORTH: f"°{u}"}, {ORTH: "."}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
TOKEN_MATCH = re.compile(r"^{n}$".format(n=_nums)).match
| 8,299 | 11.671756 | 78 | py |
spaCy | spaCy-master/spacy/lang/hy/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
class ArmenianDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Armenian(Language):
lang = "hy"
Defaults = ArmenianDefaults
__all__ = ["Armenian"]
| 317 | 17.705882 | 46 | py |
spaCy | spaCy-master/spacy/lang/hy/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hy.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Լոնդոնը Միացյալ Թագավորության մեծ քաղաք է։",
"Ո՞վ է Ֆրանսիայի նախագահը։",
"Ո՞րն է Միացյալ Նահանգների մայրաքաղաքը։",
"Ե՞րբ է ծնվել Բարաք Օբաման։",
]
| 326 | 22.357143 | 56 | py |
spaCy | spaCy-master/spacy/lang/hy/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"զրո",
"մեկ",
"երկու",
"երեք",
"չորս",
"հինգ",
"վեց",
"յոթ",
"ութ",
"ինը",
"տասը",
"տասնմեկ",
"տասներկու",
"տասներեք",
"տասնչորս",
"տասնհինգ",
"տասնվեց",
"տասնյոթ",
"տասնութ",
"տասնինը",
"քսան",
"երեսուն",
"քառասուն",
"հիսուն",
"վաթսուն",
"յոթանասուն",
"ութսուն",
"իննսուն",
"հարյուր",
"հազար",
"միլիոն",
"միլիարդ",
"տրիլիոն",
"քվինտիլիոն",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 953 | 15.736842 | 49 | py |
spaCy | spaCy-master/spacy/lang/hy/stop_words.py | STOP_WORDS = set(
"""
նա
ողջը
այստեղ
ենք
նա
էիր
որպես
ուրիշ
բոլորը
այն
այլ
նույնչափ
էի
մի
և
ողջ
ես
ոմն
հետ
նրանք
ամենքը
ըստ
ինչ-ինչ
այսպես
համայն
մի
նաև
նույնքան
դա
ովևէ
համար
այնտեղ
էին
որոնք
սույն
ինչ-որ
ամենը
նույնպիսի
ու
իր
որոշ
միևնույն
ի
այնպիսի
մենք
ամեն ոք
նույն
երբևէ
այն
որևէ
ին
այդպես
նրա
որը
վրա
դու
էինք
այդպիսի
էիք
յուրաքանչյուրը
եմ
պիտի
այդ
ամբողջը
հետո
եք
ամեն
այլ
կամ
այսքան
որ
այնպես
այսինչ
բոլոր
է
մեկնումեկը
այդչափ
այնքան
ամբողջ
երբևիցե
այնչափ
ամենայն
մյուս
այնինչ
իսկ
այդտեղ
այս
սա
են
ամեն ինչ
որևիցե
ում
մեկը
այդ
դուք
այսչափ
այդքան
այսպիսի
էր
յուրաքանչյուր
այս
մեջ
թ
""".split()
)
| 607 | 4.62963 | 17 | py |
spaCy | spaCy-master/spacy/lang/id/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class IndonesianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
suffixes = TOKENIZER_SUFFIXES
infixes = TOKENIZER_INFIXES
syntax_iterators = SYNTAX_ITERATORS
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Indonesian(Language):
lang = "id"
Defaults = IndonesianDefaults
__all__ = ["Indonesian"]
| 698 | 26.96 | 82 | py |
spaCy | spaCy-master/spacy/lang/id/_tokenizer_exceptions_list.py | ID_BASE_EXCEPTIONS = set(
"""
aba-aba
abah-abah
abal-abal
abang-abang
abar-abar
abong-abong
abrit-abrit
abrit-abritan
abu-abu
abuh-abuhan
abuk-abuk
abun-abun
acak-acak
acak-acakan
acang-acang
acap-acap
aci-aci
aci-acian
aci-acinya
aco-acoan
ad-blocker
ad-interim
ada-ada
ada-adanya
ada-adanyakah
adang-adang
adap-adapan
add-on
add-ons
adik-adik
adik-beradik
aduk-adukan
after-sales
agak-agak
agak-agih
agama-agama
agar-agar
age-related
agut-agut
air-air
air-cooled
air-to-air
ajak-ajak
ajar-ajar
aji-aji
akal-akal
akal-akalan
akan-akan
akar-akar
akar-akaran
akhir-akhir
akhir-akhirnya
aki-aki
aksi-aksi
alah-mengalahi
alai-belai
alan-alan
alang-alang
alang-alangan
alap-alap
alat-alat
ali-ali
alif-alifan
alih-alih
aling-aling
aling-alingan
alip-alipan
all-electric
all-in-one
all-out
all-time
alon-alon
alt-right
alt-text
alu-alu
alu-aluan
alun-alun
alur-alur
alur-aluran
always-on
amai-amai
amatir-amatiran
ambah-ambah
ambai-ambai
ambil-mengambil
ambreng-ambrengan
ambring-ambringan
ambu-ambu
ambung-ambung
amin-amin
amit-amit
ampai-ampai
amprung-amprungan
amung-amung
anai-anai
anak-anak
anak-anakan
anak-beranak
anak-cucu
anak-istri
ancak-ancak
ancang-ancang
ancar-ancar
andang-andang
andeng-andeng
aneh-aneh
angan-angan
anggar-anggar
anggaran-red
anggota-anggota
anggung-anggip
angin-angin
angin-anginan
angkal-angkal
angkul-angkul
angkup-angkup
angkut-angkut
ani-ani
aning-aning
anjang-anjang
anjing-anjing
anjung-anjung
anjung-anjungan
antah-berantah
antar-antar
antar-mengantar
ante-mortem
antek-antek
anter-anter
antihuru-hara
anting-anting
antung-antung
anyam-menganyam
anyang-anyang
apa-apa
apa-apaan
apel-apel
api-api
apit-apit
aplikasi-aplikasi
apotek-apotek
aprit-apritan
apu-apu
apung-apung
arah-arah
arak-arak
arak-arakan
aram-aram
arek-arek
arem-arem
ari-ari
artis-artis
aru-aru
arung-arungan
asa-asaan
asal-asalan
asal-muasal
asal-usul
asam-asaman
asas-asas
aset-aset
asmaul-husna
asosiasi-asosiasi
asuh-asuh
asyik-asyiknya
atas-mengatasi
ati-ati
atung-atung
aturan-aturan
audio-video
audio-visual
auto-brightness
auto-complete
auto-focus
auto-play
auto-update
avant-garde
awan-awan
awan-berawan
awang-awang
awang-gemawang
awar-awar
awat-awat
awik-awik
awut-awutan
ayah-anak
ayak-ayak
ayam-ayam
ayam-ayaman
ayang-ayang
ayat-ayat
ayeng-ayengan
ayun-temayun
ayut-ayutan
ba-bi-bu
back-to-back
back-up
badan-badan
bade-bade
badut-badut
bagi-bagi
bahan-bahan
bahu-membahu
baik-baik
bail-out
bajang-bajang
baji-baji
balai-balai
balam-balam
balas-berbalas
balas-membalas
bale-bale
baling-baling
ball-playing
balon-balon
balut-balut
band-band
bandara-bandara
bangsa-bangsa
bangun-bangun
bangunan-bangunan
bank-bank
bantah-bantah
bantahan-bantahan
bantal-bantal
banyak-banyak
bapak-anak
bapak-bapak
bapak-ibu
bapak-ibunya
barang-barang
barat-barat
barat-daya
barat-laut
barau-barau
bare-bare
bareng-bareng
bari-bari
barik-barik
baris-berbaris
baru-baru
baru-batu
barung-barung
basa-basi
bata-bata
batalyon-batalyon
batang-batang
batas-batas
batir-batir
batu-batu
batuk-batuk
batung-batung
bau-bauan
bawa-bawa
bayan-bayan
bayang-bayang
bayi-bayi
bea-cukai
bedeng-bedeng
bedil-bedal
bedil-bedilan
begana-begini
bek-bek
bekal-bekalan
bekerdom-kerdom
bekertak-kertak
belang-belang
belat-belit
beliau-beliau
belu-belai
belum-belum
benar-benar
benda-benda
bengang-bengut
benggal-benggil
bengkal-bengkil
bengkang-bengkok
bengkang-bengkong
bengkang-bengkung
benteng-benteng
bentuk-bentuk
benua-benua
ber-selfie
berabad-abad
berabun-rabun
beracah-acah
berada-ada
beradik-berkakak
beragah-agah
beragak-agak
beragam-ragam
beraja-raja
berakit-rakit
beraku-akuan
beralu-aluan
beralun-alun
beramah-ramah
beramah-ramahan
beramah-tamah
beramai-ramai
berambai-ambai
berambal-ambalan
berambil-ambil
beramuk-amuk
beramuk-amukan
berandai-andai
berandai-randai
beraneh-aneh
berang-berang
berangan-angan
beranggap-anggapan
berangguk-angguk
berangin-angin
berangka-angka
berangka-angkaan
berangkai-rangkai
berangkap-rangkapan
berani-berani
beranja-anja
berantai-rantai
berapi-api
berapung-apung
berarak-arakan
beras-beras
berasak-asak
berasak-asakan
berasap-asap
berasing-asingan
beratus-ratus
berawa-rawa
berawas-awas
berayal-ayalan
berayun-ayun
berbagai-bagai
berbahas-bahasan
berbahasa-bahasa
berbaik-baikan
berbait-bait
berbala-bala
berbalas-balasan
berbalik-balik
berbalun-balun
berbanjar-banjar
berbantah-bantah
berbanyak-banyak
berbarik-barik
berbasa-basi
berbasah-basah
berbatu-batu
berbayang-bayang
berbecak-becak
berbeda-beda
berbedil-bedilan
berbega-bega
berbeka-beka
berbelah-belah
berbelakang-belakangan
berbelang-belang
berbelau-belauan
berbeli-beli
berbeli-belian
berbelit-belit
berbelok-belok
berbenang-benang
berbenar-benar
berbencah-bencah
berbencol-bencol
berbenggil-benggil
berbentol-bentol
berbentong-bentong
berberani-berani
berbesar-besar
berbidai-bidai
berbiduk-biduk
berbiku-biku
berbilik-bilik
berbinar-binar
berbincang-bincang
berbingkah-bingkah
berbintang-bintang
berbintik-bintik
berbintil-bintil
berbisik-bisik
berbolak-balik
berbolong-bolong
berbondong-bondong
berbongkah-bongkah
berbuai-buai
berbual-bual
berbudak-budak
berbukit-bukit
berbulan-bulan
berbunga-bunga
berbuntut-buntut
berbunuh-bunuhan
berburu-buru
berburuk-buruk
berbutir-butir
bercabang-cabang
bercaci-cacian
bercakap-cakap
bercakar-cakaran
bercamping-camping
bercantik-cantik
bercari-cari
bercari-carian
bercarik-carik
bercarut-carut
bercebar-cebur
bercepat-cepat
bercerai-berai
bercerai-cerai
bercetai-cetai
berciap-ciap
bercikun-cikun
bercinta-cintaan
bercita-cita
berciut-ciut
bercompang-camping
berconteng-conteng
bercoreng-coreng
bercoreng-moreng
bercuang-caing
bercuit-cuit
bercumbu-cumbu
bercumbu-cumbuan
bercura-bura
bercura-cura
berdada-dadaan
berdahulu-dahuluan
berdalam-dalam
berdalih-dalih
berdampung-dampung
berdebar-debar
berdecak-decak
berdecap-decap
berdecup-decup
berdecut-decut
berdedai-dedai
berdegap-degap
berdegar-degar
berdeham-deham
berdekah-dekah
berdekak-dekak
berdekap-dekapan
berdekat-dekat
berdelat-delat
berdembai-dembai
berdembun-dembun
berdempang-dempang
berdempet-dempet
berdencing-dencing
berdendam-dendaman
berdengkang-dengkang
berdengut-dengut
berdentang-dentang
berdentum-dentum
berdentung-dentung
berdenyar-denyar
berdenyut-denyut
berdepak-depak
berdepan-depan
berderai-derai
berderak-derak
berderam-deram
berderau-derau
berderik-derik
berdering-dering
berderung-derung
berderus-derus
berdesak-desakan
berdesik-desik
berdesing-desing
berdesus-desus
berdikit-dikit
berdingkit-dingkit
berdua-dua
berduri-duri
berduru-duru
berduyun-duyun
berebut-rebut
berebut-rebutan
beregang-regang
berek-berek
berembut-rembut
berempat-empat
berenak-enak
berencel-encel
bereng-bereng
berenggan-enggan
berenteng-renteng
beresa-esaan
beresah-resah
berfoya-foya
bergagah-gagahan
bergagap-gagap
bergagau-gagau
bergalur-galur
berganda-ganda
berganjur-ganjur
berganti-ganti
bergarah-garah
bergaruk-garuk
bergaya-gaya
bergegas-gegas
bergelang-gelang
bergelap-gelap
bergelas-gelasan
bergeleng-geleng
bergemal-gemal
bergembar-gembor
bergembut-gembut
bergepok-gepok
bergerek-gerek
bergesa-gesa
bergilir-gilir
bergolak-golak
bergolek-golek
bergolong-golong
bergores-gores
bergotong-royong
bergoyang-goyang
bergugus-gugus
bergulung-gulung
bergulut-gulut
bergumpal-gumpal
bergunduk-gunduk
bergunung-gunung
berhadap-hadapan
berhamun-hamun
berhandai-handai
berhanyut-hanyut
berhari-hari
berhati-hati
berhati-hatilah
berhektare-hektare
berhilau-hilau
berhormat-hormat
berhujan-hujan
berhura-hura
beri-beri
beri-memberi
beria-ia
beria-ria
beriak-riak
beriba-iba
beribu-ribu
berigi-rigi
berimpit-impit
berindap-indap
bering-bering
beringat-ingat
beringgit-ringgit
berintik-rintik
beriring-iring
beriring-iringan
berita-berita
berjabir-jabir
berjaga-jaga
berjagung-jagung
berjalan-jalan
berjalar-jalar
berjalin-jalin
berjalur-jalur
berjam-jam
berjari-jari
berjauh-jauhan
berjegal-jegalan
berjejal-jejal
berjela-jela
berjengkek-jengkek
berjenis-jenis
berjenjang-jenjang
berjilid-jilid
berjinak-jinak
berjingkat-jingkat
berjingkik-jingkik
berjingkrak-jingkrak
berjongkok-jongkok
berjubel-jubel
berjujut-jujutan
berjulai-julai
berjumbai-jumbai
berjumbul-jumbul
berjuntai-juntai
berjurai-jurai
berjurus-jurus
berjuta-juta
berka-li-kali
berkabu-kabu
berkaca-kaca
berkaing-kaing
berkait-kaitan
berkala-kala
berkali-kali
berkamit-kamit
berkanjar-kanjar
berkaok-kaok
berkarung-karung
berkasak-kusuk
berkasih-kasihan
berkata-kata
berkatak-katak
berkecai-kecai
berkecek-kecek
berkecil-kecil
berkecil-kecilan
berkedip-kedip
berkejang-kejang
berkejap-kejap
berkejar-kejaran
berkelar-kelar
berkelepai-kelepai
berkelip-kelip
berkelit-kelit
berkelok-kelok
berkelompok-kelompok
berkelun-kelun
berkembur-kembur
berkempul-kempul
berkena-kenaan
berkenal-kenalan
berkendur-kendur
berkeok-keok
berkepak-kepak
berkepal-kepal
berkeping-keping
berkepul-kepul
berkeras-kerasan
berkering-kering
berkeritik-keritik
berkeruit-keruit
berkerut-kerut
berketai-ketai
berketak-ketak
berketak-ketik
berketap-ketap
berketap-ketip
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bertunda-tunda
bertunjuk-tunjukan
bertura-tura
berturut-turut
bertutur-tutur
beruas-ruas
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berumbai-rumbai
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bio-oil
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boleh-boleh
bom-bom
bomber-bomber
bonek-bonek
bongkar-bangkir
bongkar-membongkar
bongkar-pasang
boro-boro
bos-bos
bottom-up
box-to-box
boyo-boyo
buah-buahan
buang-buang
buat-buatan
buaya-buaya
bubun-bubun
bugi-bugi
build-up
built-in
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buka-buka
buka-bukaan
buka-tutup
bukan-bukan
bukti-bukti
buku-buku
bulan-bulan
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bulat-bulat
buli-buli
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buluh-buluh
bulus-bulus
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bunga-bungaan
bunuh-membunuh
bunyi-bunyian
bupati-bupati
bupati-wakil
buru-buru
burung-burung
burung-burungan
bus-bus
business-to-business
busur-busur
butir-butir
by-pass
bye-bye
cabang-cabang
cabik-cabik
cabik-mencabik
cabup-cawabup
caci-maki
cagub-cawagub
caing-caing
cakar-mencakar
cakup-mencakup
calak-calak
calar-balar
caleg-caleg
calo-calo
calon-calon
campang-camping
campur-campur
capres-cawapres
cara-cara
cari-cari
cari-carian
carut-marut
catch-up
cawali-cawawali
cawe-cawe
cawi-cawi
cebar-cebur
celah-celah
celam-celum
celangak-celinguk
celas-celus
celedang-celedok
celengkak-celengkok
celingak-celinguk
celung-celung
cemas-cemas
cenal-cenil
cengar-cengir
cengir-cengir
cengis-cengis
cengking-mengking
centang-perenang
cepat-cepat
ceplas-ceplos
cerai-berai
cerita-cerita
ceruk-menceruk
ceruk-meruk
cetak-biru
cetak-mencetak
cetar-ceter
check-in
check-ins
check-up
chit-chat
choki-choki
cingak-cinguk
cipika-cipiki
ciri-ciri
ciri-cirinya
cirit-birit
cita-cita
cita-citaku
close-up
closed-circuit
coba-coba
cobak-cabik
cobar-cabir
cola-cala
colang-caling
comat-comot
comot-comot
compang-camping
computer-aided
computer-generated
condong-mondong
congak-cangit
conggah-canggih
congkah-cangkih
congkah-mangkih
copak-capik
copy-paste
corak-carik
corat-coret
coreng-moreng
coret-coret
crat-crit
cross-border
cross-dressing
crypto-ransomware
cuang-caing
cublak-cublak
cubung-cubung
culik-culik
cuma-cuma
cumi-cumi
cungap-cangip
cupu-cupu
dabu-dabu
daerah-daerah
dag-dag
dag-dig-dug
daging-dagingan
dahulu-mendahului
dalam-dalam
dali-dali
dam-dam
danau-danau
dansa-dansi
dapil-dapil
dapur-dapur
dari-dari
daru-daru
dasar-dasar
datang-datang
datang-mendatangi
daun-daun
daun-daunan
dawai-dawai
dayang-dayang
dayung-mayung
debak-debuk
debu-debu
deca-core
decision-making
deep-lying
deg-degan
degap-degap
dekak-dekak
dekat-dekat
dengar-dengaran
dengking-mendengking
departemen-departemen
depo-depo
deputi-deputi
desa-desa
desa-kota
desas-desus
detik-detik
dewa-dewa
dewa-dewi
dewan-dewan
dewi-dewi
dial-up
diam-diam
dibayang-bayangi
dibuat-buat
diiming-imingi
dilebih-lebihkan
dimana-mana
dimata-matai
dinas-dinas
dinul-Islam
diobok-obok
diolok-olok
direksi-direksi
direktorat-direktorat
dirjen-dirjen
dirut-dirut
ditunggu-tunggu
divisi-divisi
do-it-yourself
doa-doa
dog-dog
doggy-style
dokok-dokok
dolak-dalik
dor-doran
dorong-mendorong
dosa-dosa
dress-up
drive-in
dua-dua
dua-duaan
dua-duanya
dubes-dubes
duduk-duduk
dugaan-dugaan
dulang-dulang
duri-duri
duta-duta
dwi-kewarganegaraan
e-arena
e-billing
e-budgeting
e-cctv
e-class
e-commerce
e-counting
e-elektronik
e-entertainment
e-evolution
e-faktur
e-filing
e-fin
e-form
e-government
e-govt
e-hakcipta
e-id
e-info
e-katalog
e-ktp
e-leadership
e-lhkpn
e-library
e-loket
e-m1
e-money
e-news
e-nisn
e-npwp
e-paspor
e-paten
e-pay
e-perda
e-perizinan
e-planning
e-polisi
e-power
e-punten
e-retribusi
e-samsat
e-sport
e-store
e-tax
e-ticketing
e-tilang
e-toll
e-visa
e-voting
e-wallet
e-warong
ecek-ecek
eco-friendly
eco-park
edan-edanan
editor-editor
editor-in-chief
efek-efek
ekonomi-ekonomi
eksekutif-legislatif
ekspor-impor
elang-elang
elemen-elemen
emak-emak
embuh-embuhan
empat-empat
empek-empek
empet-empetan
empok-empok
empot-empotan
enak-enak
encal-encal
end-to-end
end-user
endap-endap
endut-endut
endut-endutan
engah-engah
engap-engap
enggan-enggan
engkah-engkah
engket-engket
entah-berentah
enten-enten
entry-level
equity-linked
erang-erot
erat-erat
erek-erek
ereng-ereng
erong-erong
esek-esek
ex-officio
exchange-traded
exercise-induced
extra-time
face-down
face-to-face
fair-play
fakta-fakta
faktor-faktor
fakultas-fakultas
fase-fase
fast-food
feed-in
fifty-fifty
file-file
first-leg
first-team
fitur-fitur
fitur-fiturnya
fixed-income
flip-flop
flip-plop
fly-in
follow-up
foto-foto
foya-foya
fraksi-fraksi
free-to-play
front-end
fungsi-fungsi
gaba-gaba
gabai-gabai
gada-gada
gading-gading
gadis-gadis
gado-gado
gail-gail
gajah-gajah
gajah-gajahan
gala-gala
galeri-galeri
gali-gali
gali-galian
galing-galing
galu-galu
gamak-gamak
gambar-gambar
gambar-menggambar
gamit-gamitan
gampang-gampangan
gana-gini
ganal-ganal
ganda-berganda
ganjal-mengganjal
ganjil-genap
ganteng-ganteng
gantung-gantung
gapah-gopoh
gara-gara
garah-garah
garis-garis
gasak-gasakan
gatal-gatal
gaun-gaun
gawar-gawar
gaya-gayanya
gayang-gayang
ge-er
gebyah-uyah
gebyar-gebyar
gedana-gedini
gedebak-gedebuk
gedebar-gedebur
gedung-gedung
gelang-gelang
gelap-gelapan
gelar-gelar
gelas-gelas
gelembung-gelembungan
geleng-geleng
geli-geli
geliang-geliut
geliat-geliut
gembar-gembor
gembrang-gembreng
gempul-gempul
gempur-menggempur
gendang-gendang
gengsi-gengsian
genjang-genjot
genjot-genjotan
genjrang-genjreng
genome-wide
geo-politik
gerabak-gerubuk
gerak-gerik
gerak-geriknya
gerakan-gerakan
gerbas-gerbus
gereja-gereja
gereng-gereng
geriak-geriuk
gerit-gerit
gerot-gerot
geruh-gerah
getak-getuk
getem-getem
geti-geti
gial-gial
gial-giul
gila-gila
gila-gilaan
gilang-gemilang
gilap-gemilap
gili-gili
giling-giling
gilir-bergilir
ginang-ginang
girap-girap
girik-girik
giring-giring
go-auto
go-bills
go-bluebird
go-box
go-car
go-clean
go-food
go-glam
go-jek
go-kart
go-mart
go-massage
go-med
go-points
go-pulsa
go-ride
go-send
go-shop
go-tix
go-to-market
goak-goak
goal-line
gol-gol
golak-galik
gondas-gandes
gonjang-ganjing
gonjlang-ganjling
gonta-ganti
gontok-gontokan
gorap-gorap
gorong-gorong
gotong-royong
gresek-gresek
gua-gua
gual-gail
gubernur-gubernur
gudu-gudu
gula-gula
gulang-gulang
gulung-menggulung
guna-ganah
guna-guna
gundala-gundala
guntang-guntang
gunung-ganang
gunung-gemunung
gunung-gunungan
guru-guru
habis-habis
habis-habisan
hak-hak
hak-hal
hakim-hakim
hal-hal
halai-balai
half-time
hama-hama
hampir-hampir
hancur-hancuran
hancur-menghancurkan
hands-free
hands-on
hang-out
hantu-hantu
happy-happy
harap-harap
harap-harapan
hard-disk
harga-harga
hari-hari
harimau-harimau
harum-haruman
hasil-hasil
hasta-wara
hat-trick
hati-hati
hati-hatilah
head-mounted
head-to-head
head-up
heads-up
heavy-duty
hebat-hebatan
hewan-hewan
hexa-core
hidup-hidup
hidup-mati
hila-hila
hilang-hilang
hina-menghinakan
hip-hop
hiru-biru
hiru-hara
hiruk-pikuk
hitam-putih
hitung-hitung
hitung-hitungan
hormat-menghormati
hot-swappable
hotel-hotel
how-to
hubar-habir
hubaya-hubaya
hukum-red
hukuman-hukuman
hula-hoop
hula-hula
hulu-hilir
humas-humas
hura-hura
huru-hara
ibar-ibar
ibu-anak
ibu-ibu
icak-icak
icip-icip
idam-idam
ide-ide
igau-igauan
ikan-ikan
ikut-ikut
ikut-ikutan
ilam-ilam
ilat-ilatan
ilmu-ilmu
imbang-imbangan
iming-iming
imut-imut
inang-inang
inca-binca
incang-incut
industri-industri
ingar-bingar
ingar-ingar
ingat-ingat
ingat-ingatan
ingau-ingauan
inggang-inggung
injak-injak
input-output
instansi-instansi
instant-on
instrumen-instrumen
inter-governmental
ira-ira
irah-irahan
iras-iras
iring-iringan
iris-irisan
isak-isak
isat-bb
iseng-iseng
istana-istana
istri-istri
isu-isu
iya-iya
jabatan-jabatan
jadi-jadian
jagoan-jagoan
jaja-jajaan
jaksa-jaksa
jala-jala
jalan-jalan
jali-jali
jalin-berjalin
jalin-menjalin
jam-jam
jamah-jamahan
jambak-jambakan
jambu-jambu
jampi-jampi
janda-janda
jangan-jangan
janji-janji
jarang-jarang
jari-jari
jaring-jaring
jarum-jarum
jasa-jasa
jatuh-bangun
jauh-dekat
jauh-jauh
jawi-jawi
jebar-jebur
jebat-jebatan
jegal-jegalan
jejak-jejak
jelang-menjelang
jelas-jelas
jelur-jelir
jembatan-jembatan
jenazah-jenazah
jendal-jendul
jenderal-jenderal
jenggar-jenggur
jenis-jenis
jenis-jenisnya
jentik-jentik
jerah-jerih
jinak-jinak
jiwa-jiwa
joli-joli
jolong-jolong
jongkang-jangking
jongkar-jangkir
jongkat-jangkit
jor-joran
jotos-jotosan
juak-juak
jual-beli
juang-juang
julo-julo
julung-julung
julur-julur
jumbai-jumbai
jungkang-jungkit
jungkat-jungkit
jurai-jurai
kabang-kabang
kabar-kabari
kabir-kabiran
kabruk-kabrukan
kabu-kabu
kabupaten-kabupaten
kabupaten-kota
kaca-kaca
kacang-kacang
kacang-kacangan
kacau-balau
kadang-kadang
kader-kader
kades-kades
kadis-kadis
kail-kail
kain-kain
kait-kait
kakak-adik
kakak-beradik
kakak-kakak
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kakek-nenek
kaki-kaki
kala-kala
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kampung-kampung
kampus-kampus
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kanan-kiri
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kanwil-kanwil
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karang-karangan
karang-mengarang
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karut-marut
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kasus-kasus
kata-kata
katang-katang
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kedubes-kedubes
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kegelap-gelapan
kegiatan-kegiatan
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kehitam-hitaman
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kejar-kejaran
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kejutan-kejutan
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kemenpan-rb
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klub-klub
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knock-on
knock-out
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koleh-koleh
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komite-komite
komoditas-komoditas
kongko-kongko
konsulat-konsulat
konsultan-konsultan
kontal-kantil
kontang-kanting
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konvensi-konvensi
kopat-kapit
koperasi-koperasi
kopi-kopi
koran-koran
koreng-koreng
kos-kosan
kosak-kasik
kota-kota
kota-wakil
kotak-katik
kotak-kotak
koyak-koyak
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kuat-kuat
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kuda-kuda
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kulum-kulum
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kura-kura
kurang-kurang
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kyai-kyai
laba-laba
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lapat-lapat
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lentang-lentung
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letah-letai
lete-lete
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letum-letum
letup-letup
leyeh-leyeh
liang-liuk
liang-liut
liar-liar
liat-liut
lidah-lidah
life-toxins
liga-liga
light-emitting
lika-liku
lil-alamin
lilin-lilin
line-up
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lipat-melipat
liquid-cooled
lithium-ion
lithium-polymer
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lock-up
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lopak-lopak
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low-end
low-light
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low-pass
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lumi-lumi
luntang-lantung
lupa-lupa
lupa-lupaan
lurah-camat
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mabuk-mabukan
mabul-mabul
macam-macam
macan-macanan
machine-to-machine
mafia-mafia
mahasiswa-mahasiswi
mahasiswa/i
mahi-mahi
main-main
main-mainan
main-mainlah
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maju-mundur
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makan-makan
makan-makanan
makanan-red
make-up
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maki-makian
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malar-malar
malas-malasan
mali-mali
malu-malu
mama-mama
man-in-the-middle
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manajer-manajer
manik-manik
manis-manis
manis-manisan
marah-marah
mark-up
mas-mas
masa-masa
masak-masak
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mash-up
masing-masing
masjid-masjid
masuk-keluar
mat-matan
mata-mata
match-fixing
mati-mati
mati-matian
maya-maya
mayat-mayat
mayday-mayday
media-media
mega-bintang
mega-tsunami
megal-megol
megap-megap
meger-meger
megrek-megrek
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melayap-layap
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meliuk-liuk
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melompat-lompat
meloncat-loncat
melonco-lonco
melongak-longok
melonjak-lonjak
memacak-macak
memada-madai
memadan-madan
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memancit-mancitkan
memandai-mandai
memanggil-manggil
memanis-manis
memanjut-manjut
memantas-mantas
memasak-masak
memata-matai
mematah-matah
mematuk-matuk
mematut-matut
memau-mau
memayah-mayahkan
membaca-baca
membacah-bacah
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membalik-balik
membangkit-bangkit
membarut-barut
membawa-bawa
membayang-bayangi
membayang-bayangkan
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membelai-belai
membeli-beli
membelit-belitkan
membelu-belai
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membenar-benari
memberai-beraikan
membesar-besar
membesar-besarkan
membikin-bikin
membilah-bilah
membolak-balikkan
membongkar-bangkir
membongkar-bongkar
membuang-buang
membuat-buat
membulan-bulani
membunga-bungai
membungkuk-bungkuk
memburu-buru
memburu-burukan
memburuk-burukkan
memelintir-melintir
memencak-mencak
memencar-mencar
memercik-mercik
memetak-metak
memetang-metangkan
memetir-metir
memijar-mijar
memikir-mikir
memikir-mikirkan
memilih-milih
memilin-milin
meminang-minang
meminta-minta
memisah-misahkan
memontang-mantingkan
memorak-perandakan
memorak-porandakan
memotong-motong
memperamat-amat
memperamat-amatkan
memperbagai-bagaikan
memperganda-gandakan
memperganduh-ganduhkan
memperimpit-impitkan
memperkuda-kudakan
memperlengah-lengah
memperlengah-lengahkan
mempermacam-macamkan
memperolok-olok
memperolok-olokkan
mempersama-samakan
mempertubi-tubi
mempertubi-tubikan
memperturut-turutkan
memuja-muja
memukang-mukang
memulun-mulun
memundi-mundi
memundi-mundikan
memutar-mutar
memuyu-muyu
men-tweet
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menakut-nakuti
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menanti-nanti
menari-nari
mencabik-cabik
mencabik-cabikkan
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mencaing-caing
mencak-mencak
mencakup-cakup
mencapak-capak
mencari-cari
mencarik-carik
mencarik-carikkan
mencarut-carut
mencengis-cengis
mencepak-cepak
mencepuk-cepuk
mencerai-beraikan
mencetai-cetai
menciak-ciak
menciap-ciap
menciar-ciar
mencita-citakan
mencium-cium
menciut-ciut
mencla-mencle
mencoang-coang
mencoba-coba
mencocok-cocok
mencolek-colek
menconteng-conteng
mencubit-cubit
mencucuh-cucuh
mencucuh-cucuhkan
mencuri-curi
mendecap-decap
mendegam-degam
mendengar-dengar
mendengking-dengking
mendengus-dengus
mendengut-dengut
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menderak-derakkan
menderau-derau
menderu-deru
mendesas-desuskan
mendesus-desus
mendetap-detap
mendewa-dewakan
mendudu-dudu
menduga-duga
menebu-nebu
menegur-neguri
menepak-nepak
menepak-nepakkan
mengabung-ngabung
mengaci-acikan
mengacu-acu
mengada-ada
mengada-ngada
mengadang-adangi
mengaduk-aduk
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mengagak-agihkan
mengagut-agut
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mengalang-alangi
mengali-ali
mengalur-alur
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mengamat-amati
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mengambung-ambung
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mengancar-ancar
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mengangan-angankan
mengangguk-angguk
menganggut-anggut
mengangin-anginkan
mengangkat-angkat
menganjung-anjung
menganjung-anjungkan
mengap-mengap
mengapa-apai
mengapi-apikan
mengarah-arahi
mengarang-ngarang
mengata-ngatai
mengatup-ngatupkan
mengaum-aum
mengaum-aumkan
mengejan-ejan
mengejar-ngejar
mengejut-ngejuti
mengelai-ngelai
mengelepik-ngelepik
mengelip-ngelip
mengelu-elukan
mengelus-elus
mengembut-embut
mengempas-empaskan
mengenap-enapkan
mengendap-endap
mengenjak-enjak
mengentak-entak
mengentak-entakkan
mengepak-ngepak
mengepak-ngepakkan
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mengerjap-ngerjap
mengerling-ngerling
mengertak-ngertakkan
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menggaba-gabai
menggali-gali
menggalur-galur
menggamak-gamak
menggamit-gamitkan
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menggapai-gapaikan
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menggebu-gebu
menggebyah-uyah
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menggelepar-geleparkan
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menggodot-godot
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menggoreng-goreng
menggosok-gosok
menggoyang-goyangkan
mengguit-guit
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mengimbak-imbak
mengiming-iming
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mengingat-ingat
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mengiras-irasi
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mengodol-odol
mengogok-ogok
mengolak-alik
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mengolok-olok
mengombang-ambing
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mengongkok-ongkok
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mengoret-oret
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mengoyak-ngoyakkan
mengoyak-oyak
menguar-nguarkan
menguar-uarkan
mengubah-ubah
mengubek-ubek
menguber-uber
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mengubrak-abrik
mengucar-ngacirkan
mengucek-ngucek
mengucek-ucek
menguik-uik
menguis-uis
mengulang-ulang
mengulas-ulas
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mengulum-ngulum
mengulur-ulur
menguman-uman
mengumbang-ambingkan
mengumpak-umpak
mengungkat-ungkat
mengungkit-ungkit
mengupa-upa
mengurik-urik
mengusil-usil
mengusil-usilkan
mengutak-atik
mengutak-ngatikkan
mengutik-ngutik
mengutik-utik
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menimbang-nimbang
menimbun-nimbun
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menitar-nitarkan
meniup-niup
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menjadi-jadikan
menjedot-jedotkan
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menjengit-jengit
menjerit-jerit
menjilat-jilat
menjungkat-jungkit
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menonjol-nonjolkan
mentah-mentah
mentang-mentang
menteri-menteri
mentul-mentul
menuding-nuding
menumpah-numpahkan
menunda-nunda
menunduk-nunduk
menusuk-nusuk
menyala-nyala
menyama-nyama
menyama-nyamai
menyambar-nyambar
menyangkut-nyangkutkan
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menyanjung-nyanjungkan
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menyelang-nyelingkan
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menyentuh-nyentuh
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menyerak-nyerakkan
menyeret-nyeret
menyeru-nyerukan
menyetel-nyetel
menyia-nyiakan
menyibak-nyibak
menyobek-nyobek
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menyuruk-nyuruk
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merah-hitam
merah-merah
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merangkak-rangkak
merasa-rasai
merata-ratakan
meraung-raung
meraung-raungkan
merayau-rayau
merayu-rayu
mercak-mercik
mercedes-benz
merek-merek
mereka-mereka
mereka-reka
merelap-relap
merem-merem
meremah-remah
meremas-remas
meremeh-temehkan
merempah-rempah
merempah-rempahi
merengek-rengek
merengeng-rengeng
merenik-renik
merenta-renta
merenyai-renyai
meresek-resek
merintang-rintang
merintik-rintik
merobek-robek
meronta-ronta
meruap-ruap
merubu-rubu
merungus-rungus
merungut-rungut
meta-analysis
metode-metode
mewanti-wanti
mewarna-warnikan
meyakin-yakini
mid-range
mid-size
miju-miju
mikro-kecil
mimpi-mimpi
minggu-minggu
minta-minta
minuman-minuman
mixed-use
mobil-mobil
mobile-first
mobile-friendly
moga-moga
mola-mola
momen-momen
mondar-mandir
monyet-monyet
morak-marik
morat-marit
move-on
muda-muda
muda-mudi
muda/i
mudah-mudahan
muka-muka
mula-mula
multiple-output
muluk-muluk
mulut-mulutan
mumi-mumi
mundur-mundur
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murid-muridnya
musda-musda
museum-museum
muslim-muslimah
musuh-musuh
musuh-musuhnya
nabi-nabi
nada-nadanya
naga-naga
naga-naganya
naik-naik
naik-turun
nakal-nakalan
nama-nama
nanti-nantian
nanya-nanya
nasi-nasi
nasib-nasiban
near-field
negara-negara
negera-negara
negeri-negeri
negeri-red
neka-neka
nekat-nekat
neko-neko
nenek-nenek
neo-liberalisme
next-gen
next-generation
ngeang-ngeang
ngeri-ngeri
nggak-nggak
ngobrol-ngobrol
ngumpul-ngumpul
nilai-nilai
nine-dash
nipa-nipa
nong-nong
norma-norma
novel-novel
nyai-nyai
nyolong-nyolong
nyut-nyutan
ob-gyn
obat-obat
obat-obatan
objek-objek
obok-obok
obrak-abrik
octa-core
odong-odong
oedipus-kompleks
off-road
ogah-agih
ogah-ogah
ogah-ogahan
ogak-agik
ogak-ogak
ogoh-ogoh
olak-alik
olak-olak
olang-aling
olang-alingan
ole-ole
oleh-oleh
olok-olok
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om-om
ombang-ambing
omni-channel
on-board
on-demand
on-fire
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on-off
on-premises
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on-screen
on-the-go
onde-onde
ondel-ondel
ondos-ondos
one-click
one-to-one
one-touch
one-two
oneng-oneng
ongkang-ongkang
ongol-ongol
online-to-offline
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onyah-anyih
onyak-anyik
opak-apik
opsi-opsi
opt-in
orak-arik
orang-aring
orang-orang
orang-orangan
orat-oret
organisasi-organisasi
ormas-ormas
orok-orok
orong-orong
oseng-oseng
otak-atik
otak-otak
otak-otakan
over-heating
over-the-air
over-the-top
pa-pa
pabrik-pabrik
padi-padian
pagi-pagi
pagi-sore
pajak-pajak
paket-paket
palas-palas
palato-alveolar
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panel-panel
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partai-partai
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pasar-pasar
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paus-paus
paut-memaut
pay-per-click
paya-paya
pdi-p
pecah-pecah
pecat-pecatan
peer-to-peer
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pekak-pekak
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pelabuhan-pelabuhan
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pemeluk-pemeluknya
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pemerintah-red
pemerintah-swasta
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perempat-final
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pertimbangan-pertimbangan
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piala-piala
piat-piut
pick-up
picture-in-picture
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pijar-pijar
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pil-pil
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pindah-pindah
ping-pong
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planet-planet
play-off
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plonga-plongo
plug-in
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poin-poin
point-of-sale
point-of-sales
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polang-paling
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pop-up
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pos-pos
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praktik-praktik
produk-produk
program-program
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pull-up
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puruk-parak
pusar-pusar
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push-to-talk
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push-ups
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puskesmas-puskesmas
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putih-hitam
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putra-putri
putra/i
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putus-putus
putusan-putusan
puvi-puvi
quad-core
raba-rabaan
raba-rubu
rada-rada
radio-frequency
ragu-ragu
rahasia-rahasiaan
raja-raja
rama-rama
ramai-ramai
ramalan-ramalan
rambeh-rambeh
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rame-rame
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rangkul-merangkul
rango-rango
rap-rap
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read-only
real-life
real-time
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rebah-rebahan
rebas-rebas
red-eye
redam-redam
redep-redup
rehab-rekon
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remeh-temeh
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ribut-ribut
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ride-sharing
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romol-romol
rompang-romping
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royal-royalan
royo-royo
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rundu-rundu
runggu-rangga
runner-up
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sarana-prasarana
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sauk-sauk
sayang-sayang
sayap-sayap
sayup-menyayup
sayup-sayup
sayur-mayur
sayur-sayuran
sci-fi
seagak-agak
seakal-akal
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sealak-alak
seari-arian
sebaik-baiknya
sebelah-menyebelah
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seboleh-bolehnya
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sehari-harian
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sejadi-jadinya
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sekali-sekali
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sekosong-kosongnya
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self-healing
self-help
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semah-semah
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sen-senan
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senggang-tenggang
senggol-menyenggol
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sewa-menyewa
sewaktu-waktu
sewenang-wenang
sewot-sewotan
shabu-shabu
short-term
short-throw
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siang-siang
siap-siap
siapa-siapa
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side-by-side
sign-in
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sikut-sikutan
silah-silah
silang-menyilang
silir-semilir
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sinar-menyinar
sinar-seminar
sinar-suminar
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singgah-menyinggah
single-core
sipil-militer
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sirat-sirat
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sisi-sisi
siswa-siswa
siswa-siswi
siswa/i
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situ-situ
situs-situs
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six-speed
slintat-slintut
slo-mo
slow-motion
snap-on
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sok-sokan
solek-menyolek
solid-state
sorak-sorai
sorak-sorak
sore-sore
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soya-soya
spill-resistant
split-screen
sponsor-sponsor
sponsor-sponsoran
srikandi-srikandi
staf-staf
stand-by
stand-up
start-up
stasiun-stasiun
state-owned
striker-striker
studi-studi
suam-suam
suami-isteri
suami-istri
suami-suami
suang-suang
suara-suara
sudin-sudin
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sunting-menyunting
super-damai
super-rahasia
super-sub
supply-demand
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surat-menyurat
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suruh-suruhan
suruk-surukan
susul-menyusul
suwir-suwir
syarat-syarat
system-on-chip
t-shirt
t-shirts
tabar-tabar
tabir-mabir
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tabun-menabun
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tahu-tahu
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takang-takik
take-off
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takut-takutan
tali-bertali
tali-tali
talun-temalun
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tangga-tangga
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tanggul-tanggul
tanggung-menanggung
tanggung-tanggung
tank-tank
tante-tante
tanya-jawab
tapa-tapa
tapak-tapak
tari-menari
tari-tarian
tarik-menarik
tarik-ulur
tata-tertib
tatah-tatah
tau-tau
tawa-tawa
tawak-tawak
tawang-tawang
tawar-menawar
tawar-tawar
tayum-temayum
tebak-tebakan
tebu-tebu
tedong-tedong
tegak-tegak
tegerbang-gerbang
teh-tehan
tek-tek
teka-teki
teknik-teknik
teman-teman
teman-temanku
temas-temas
tembak-menembak
temeh-temeh
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tempat-tempat
tempo-tempo
temut-temut
tenang-tenang
tengah-tengah
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tengok-menengok
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teralang-alang
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terambung-ambung
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terang-terangan
teranggar-anggar
terangguk-angguk
teranggul-anggul
terangin-angin
terangkup-angkup
teranja-anja
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terayan-rayan
terayap-rayap
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terbata-bata
terbatuk-batuk
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terbeda-bedakan
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terbirit-birit
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terburu-buru
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tercoreng-moreng
tercungap-cungap
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terduga-duga
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terembut-embut
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terempas-empas
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tergeleng-geleng
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tergugu-gugu
terguling-guling
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teringat-ingat
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terkagum-kagum
terkaing-kaing
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terkampul-kampul
terkanjar-kanjar
terkantuk-kantuk
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terkapung-kapung
terkatah-katah
terkatung-katung
terkecap-kecap
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terkedip-kedip
terkejar-kejar
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terkekeh-kekeh
terkekek-kekek
terkelinjat-kelinjat
terkelip-kelip
terkempul-kempul
terkemut-kemut
terkencar-kencar
terkencing-kencing
terkentut-kentut
terkepak-kepak
terkesot-kesot
terkesut-kesut
terkial-kial
terkijai-kijai
terkikih-kikih
terkikik-kikik
terkincak-kincak
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terkinja-kinja
terkirai-kirai
terkitar-kitar
terkocoh-kocoh
terkojol-kojol
terkokol-kokol
terkosel-kosel
terkotak-kotak
terkoteng-koteng
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terliuk-liuk
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terlongong-longong
terlunta-lunta
termangu-mangu
termanja-manja
termata-mata
termengah-mengah
termenung-menung
termimpi-mimpi
termonyong-monyong
ternanti-nanti
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teroleng-oleng
terombang-ambing
terpalit-palit
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terpecah-pecah
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terpencar-pencar
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terpikau-pikau
terpilah-pilah
terpinga-pinga
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terpingkau-pingkau
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tersedih-sedih
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tersoja-soja
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tertitar-titar
terumbang-ambing
terumbang-umbang
terungkap-ungkap
terus-menerus
terus-terusan
tete-a-tete
text-to-speech
think-tank
think-thank
third-party
third-person
three-axis
three-point
tiap-tiap
tiba-tiba
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tidur-tidur
tidur-tiduran
tie-dye
tie-in
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tikam-menikam
tiki-taka
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tilik-menilik
tim-tim
timah-timah
timang-timangan
timbang-menimbang
time-lapse
timpa-menimpa
timu-timu
timun-timunan
timur-barat
timur-laut
timur-tenggara
tindih-bertindih
tindih-menindih
tinjau-meninjau
tinju-meninju
tip-off
tipu-tipu
tiru-tiruan
titik-titik
titik-titiknya
tiup-tiup
to-do
tokak-takik
toko-toko
tokoh-tokoh
tokok-menokok
tolak-menolak
tolong-menolong
tong-tong
top-level
top-up
totol-totol
touch-screen
trade-in
training-camp
trans-nasional
treble-winner
tri-band
trik-trik
triple-core
truk-truk
tua-tua
tuan-tuan
tuang-tuang
tuban-tuban
tubuh-tubuh
tujuan-tujuan
tuk-tuk
tukang-menukang
tukar-menukar
tulang-belulang
tulang-tulangan
tuli-tuli
tulis-menulis
tumbuh-tumbuhan
tumpang-tindih
tune-up
tunggang-tunggik
tunggang-tungging
tunggang-tunggit
tunggul-tunggul
tunjuk-menunjuk
tupai-tupai
tupai-tupaian
turi-turian
turn-based
turnamen-turnamen
turun-temurun
turut-menurut
turut-turutan
tuyuk-tuyuk
twin-cam
twin-turbocharged
two-state
two-step
two-tone
u-shape
uang-uangan
uar-uar
ubek-ubekan
ubel-ubel
ubrak-abrik
ubun-ubun
ubur-ubur
uci-uci
udang-undang
udap-udapan
ugal-ugalan
uget-uget
uir-uir
ujar-ujar
uji-coba
ujung-ujung
ujung-ujungnya
uka-uka
ukir-mengukir
ukir-ukiran
ula-ula
ulak-ulak
ulam-ulam
ulang-alik
ulang-aling
ulang-ulang
ulap-ulap
ular-ular
ular-ularan
ulek-ulek
ulu-ulu
ulung-ulung
umang-umang
umbang-ambing
umbi-umbian
umbul-umbul
umbut-umbut
uncang-uncit
undak-undakan
undang-undang
undang-undangnya
unduk-unduk
undung-undung
undur-undur
unek-unek
ungah-angih
unggang-anggit
unggat-unggit
unggul-mengungguli
ungkit-ungkit
unit-unit
universitas-universitas
unsur-unsur
untang-anting
unting-unting
untung-untung
untung-untungan
upah-mengupah
upih-upih
upside-down
ura-ura
uran-uran
urat-urat
uring-uringan
urup-urup
urup-urupan
urus-urus
usaha-usaha
user-user
user-useran
utak-atik
utang-piutang
utang-utang
utar-utar
utara-jauh
utara-selatan
uter-uter
utusan-utusan
v-belt
v-neck
value-added
very-very
video-video
visi-misi
visi-misinya
voa-islam
voice-over
volt-ampere
wajah-wajah
wajar-wajar
wake-up
wakil-wakil
walk-in
walk-out
wangi-wangian
wanita-wanita
wanti-wanti
wara-wara
wara-wiri
warna-warna
warna-warni
was-was
water-cooled
web-based
wide-angle
wilayah-wilayah
win-win
wira-wiri
wora-wari
work-life
world-class
yang-yang
yayasan-yayasan
year-on-year
yel-yel
yo-yo
zam-zam
zig-zag
""".split()
)
| 53,599 | 12.733026 | 25 | py |
spaCy | spaCy-master/spacy/lang/id/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.id.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Indonesia merupakan negara kepulauan yang kaya akan budaya.",
"Berapa banyak warga yang dibutuhkan saat kerja bakti?",
"Penyaluran pupuk berasal dari lima lokasi yakni Bontang, Kalimantan Timur, Surabaya, Banyuwangi, Semarang, dan Makassar.",
"PT Pupuk Kaltim telah menyalurkan 274.707 ton pupuk bersubsidi ke wilayah penyaluran di 14 provinsi.",
"Jakarta adalah kota besar yang nyaris tidak pernah tidur."
"Kamu ada di mana semalam?",
"Siapa yang membeli makanan ringan tersebut?",
"Siapa presiden pertama Republik Indonesia?",
]
| 726 | 37.263158 | 127 | py |
spaCy | spaCy-master/spacy/lang/id/lex_attrs.py | import unicodedata
from ...attrs import IS_CURRENCY, LIKE_NUM
from .punctuation import LIST_CURRENCY
_num_words = [
"nol",
"satu",
"dua",
"tiga",
"empat",
"lima",
"enam",
"tujuh",
"delapan",
"sembilan",
"sepuluh",
"sebelas",
"belas",
"puluh",
"ratus",
"ribu",
"juta",
"miliar",
"biliun",
"triliun",
"kuadriliun",
"kuintiliun",
"sekstiliun",
"septiliun",
"oktiliun",
"noniliun",
"desiliun",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
return True
if text.count("-") == 1:
_, num = text.split("-")
if num.isdigit() or num in _num_words:
return True
return False
def is_currency(text):
if text in LIST_CURRENCY:
return True
for char in text:
if unicodedata.category(char) != "Sc":
return False
return True
LEX_ATTRS = {IS_CURRENCY: is_currency, LIKE_NUM: like_num}
| 1,275 | 18.044776 | 58 | py |
spaCy | spaCy-master/spacy/lang/id/punctuation.py | from ..char_classes import ALPHA, _currency, _units, merge_chars, split_chars
from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
_units = (
_units + "s bit Gbps Mbps mbps Kbps kbps ƒ ppi px "
"Hz kHz MHz GHz mAh "
"ratus rb ribu ribuan "
"juta jt jutaan mill?iar million bil[l]?iun bilyun billion "
)
_currency = _currency + r" USD Rp IDR RMB SGD S\$"
_months = (
"Januari Februari Maret April Mei Juni Juli Agustus September "
"Oktober November Desember January February March May June "
"July August October December Jan Feb Mar Jun Jul Aug Sept "
"Oct Okt Nov Des "
)
UNITS = merge_chars(_units)
CURRENCY = merge_chars(_currency)
HTML_PREFIX = r"<(b|strong|i|em|p|span|div|br)\s?/>|<a([^>]+)>"
HTML_SUFFIX = r"</(b|strong|i|em|p|span|div|a)>"
MONTHS = merge_chars(_months)
LIST_CURRENCY = split_chars(_currency)
_prefixes = list(TOKENIZER_PREFIXES)
_prefixes.remove("#") # hashtag
_prefixes = _prefixes + LIST_CURRENCY + [HTML_PREFIX] + ["/", "—"]
_suffixes = (
TOKENIZER_SUFFIXES
+ [r"\-[Nn]ya", "-[KkMm]u", "[—-]"]
+ [
# disabled: variable width currency variable
# r"(?<={c})(?:[0-9]+)".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[0-9])%",
# disabled: variable width HTML_SUFFIX variable
# r"(?<=[0-9{a}]{h})(?:[\.,:-])".format(a=ALPHA, h=HTML_SUFFIX),
r"(?<=[0-9{a}])(?:{h})".format(a=ALPHA, h=HTML_SUFFIX),
]
)
_infixes = TOKENIZER_INFIXES + [
r"(?<=[0-9])[\\/](?=[0-9%-])",
r"(?<=[0-9])%(?=[{a}0-9/])".format(a=ALPHA),
# disabled: variable width units variable
# r"(?<={u})[\/-](?=[0-9])".format(u=UNITS),
# disabled: variable width months variable
# r"(?<={m})[\/-](?=[0-9])".format(m=MONTHS),
r'(?<=[0-9)][.,])"(?=[0-9])',
r'(?<=[{a})][.,\'])["—](?=[{a}])'.format(a=ALPHA),
r"(?<=[{a}])-(?=[0-9])".format(a=ALPHA),
r"(?<=[0-9])-(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[\/-](?={c}|[{a}])".format(a=ALPHA, c=CURRENCY),
]
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes
| 2,131 | 33.95082 | 83 | py |
spaCy | spaCy-master/spacy/lang/id/stop_words.py | STOP_WORDS = set(
"""
ada adalah adanya adapun agak agaknya agar akan akankah akhir akhiri akhirnya
aku akulah amat amatlah anda andalah antar antara antaranya apa apaan apabila
apakah apalagi apatah artinya asal asalkan atas atau ataukah ataupun awal
awalnya
bagai bagaikan bagaimana bagaimanakah bagaimanapun bagi bagian bahkan bahwa
bahwasanya baik bakal bakalan balik banyak bapak baru bawah beberapa begini
beginian beginikah beginilah begitu begitukah begitulah begitupun bekerja
belakang belakangan belum belumlah benar benarkah benarlah berada berakhir
berakhirlah berakhirnya berapa berapakah berapalah berapapun berarti berawal
berbagai berdatangan beri berikan berikut berikutnya berjumlah berkali-kali
berkata berkehendak berkeinginan berkenaan berlainan berlalu berlangsung
berlebihan bermacam bermacam-macam bermaksud bermula bersama bersama-sama
bersiap bersiap-siap bertanya bertanya-tanya berturut berturut-turut bertutur
berujar berupa besar betul betulkah biasa biasanya bila bilakah bisa bisakah
boleh bolehkah bolehlah buat bukan bukankah bukanlah bukannya bulan bung
cara caranya cukup cukupkah cukuplah cuma
dahulu dalam dan dapat dari daripada datang dekat demi demikian demikianlah
dengan depan di dia diakhiri diakhirinya dialah diantara diantaranya diberi
diberikan diberikannya dibuat dibuatnya didapat didatangkan digunakan
diibaratkan diibaratkannya diingat diingatkan diinginkan dijawab dijelaskan
dijelaskannya dikarenakan dikatakan dikatakannya dikerjakan diketahui
diketahuinya dikira dilakukan dilalui dilihat dimaksud dimaksudkan
dimaksudkannya dimaksudnya diminta dimintai dimisalkan dimulai dimulailah
dimulainya dimungkinkan dini dipastikan diperbuat diperbuatnya dipergunakan
diperkirakan diperlihatkan diperlukan diperlukannya dipersoalkan dipertanyakan
dipunyai diri dirinya disampaikan disebut disebutkan disebutkannya disini
disinilah ditambahkan ditandaskan ditanya ditanyai ditanyakan ditegaskan
ditujukan ditunjuk ditunjuki ditunjukkan ditunjukkannya ditunjuknya dituturkan
dituturkannya diucapkan diucapkannya diungkapkan dong dua dulu
empat enggak enggaknya entah entahlah
guna gunakan
hal hampir hanya hanyalah hari harus haruslah harusnya hendak hendaklah
hendaknya hingga
ia ialah ibarat ibaratkan ibaratnya ibu ikut ingat ingat-ingat ingin inginkah
inginkan ini inikah inilah itu itukah itulah
jadi jadilah jadinya jangan jangankan janganlah jauh jawab jawaban jawabnya
jelas jelaskan jelaslah jelasnya jika jikalau juga jumlah jumlahnya justru
kala kalau kalaulah kalaupun kalian kami kamilah kamu kamulah kan kapan
kapankah kapanpun karena karenanya kasus kata katakan katakanlah katanya ke
keadaan kebetulan kecil kedua keduanya keinginan kelamaan kelihatan
kelihatannya kelima keluar kembali kemudian kemungkinan kemungkinannya kenapa
kepada kepadanya kesampaian keseluruhan keseluruhannya keterlaluan ketika
khususnya kini kinilah kira kira-kira kiranya kita kitalah kok kurang
lagi lagian lah lain lainnya lalu lama lamanya lanjut lanjutnya lebih lewat
lima luar
macam maka makanya makin malah malahan mampu mampukah mana manakala manalagi
masa masalah masalahnya masih masihkah masing masing-masing mau maupun
melainkan melakukan melalui melihat melihatnya memang memastikan memberi
memberikan membuat memerlukan memihak meminta memintakan memisalkan memperbuat
mempergunakan memperkirakan memperlihatkan mempersiapkan mempersoalkan
mempertanyakan mempunyai memulai memungkinkan menaiki menambahkan menandaskan
menanti menanti-nanti menantikan menanya menanyai menanyakan mendapat
mendapatkan mendatang mendatangi mendatangkan menegaskan mengakhiri mengapa
mengatakan mengatakannya mengenai mengerjakan mengetahui menggunakan
menghendaki mengibaratkan mengibaratkannya mengingat mengingatkan menginginkan
mengira mengucapkan mengucapkannya mengungkapkan menjadi menjawab menjelaskan
menuju menunjuk menunjuki menunjukkan menunjuknya menurut menuturkan
menyampaikan menyangkut menyatakan menyebutkan menyeluruh menyiapkan merasa
mereka merekalah merupakan meski meskipun meyakini meyakinkan minta mirip
misal misalkan misalnya mula mulai mulailah mulanya mungkin mungkinkah
nah naik namun nanti nantinya nyaris nyatanya
oleh olehnya
pada padahal padanya pak paling panjang pantas para pasti pastilah penting
pentingnya per percuma perlu perlukah perlunya pernah persoalan pertama
pertama-tama pertanyaan pertanyakan pihak pihaknya pukul pula pun punya
rasa rasanya rata rupanya
saat saatnya saja sajalah saling sama sama-sama sambil sampai sampai-sampai
sampaikan sana sangat sangatlah satu saya sayalah se sebab sebabnya sebagai
sebagaimana sebagainya sebagian sebaik sebaik-baiknya sebaiknya sebaliknya
sebanyak sebegini sebegitu sebelum sebelumnya sebenarnya seberapa sebesar
sebetulnya sebisanya sebuah sebut sebutlah sebutnya secara secukupnya sedang
sedangkan sedemikian sedikit sedikitnya seenaknya segala segalanya segera
seharusnya sehingga seingat sejak sejauh sejenak sejumlah sekadar sekadarnya
sekali sekali-kali sekalian sekaligus sekalipun sekarang sekarang sekecil
seketika sekiranya sekitar sekitarnya sekurang-kurangnya sekurangnya sela
selain selaku selalu selama selama-lamanya selamanya selanjutnya seluruh
seluruhnya semacam semakin semampu semampunya semasa semasih semata semata-mata
semaunya sementara semisal semisalnya sempat semua semuanya semula sendiri
sendirian sendirinya seolah seolah-olah seorang sepanjang sepantasnya
sepantasnyalah seperlunya seperti sepertinya sepihak sering seringnya serta
serupa sesaat sesama sesampai sesegera sesekali seseorang sesuatu sesuatunya
sesudah sesudahnya setelah setempat setengah seterusnya setiap setiba setibanya
setidak-tidaknya setidaknya setinggi seusai sewaktu siap siapa siapakah
siapapun sini sinilah soal soalnya suatu sudah sudahkah sudahlah supaya
tadi tadinya tahu tahun tak tambah tambahnya tampak tampaknya tandas tandasnya
tanpa tanya tanyakan tanyanya tapi tegas tegasnya telah tempat tengah tentang
tentu tentulah tentunya tepat terakhir terasa terbanyak terdahulu terdapat
terdiri terhadap terhadapnya teringat teringat-ingat terjadi terjadilah
terjadinya terkira terlalu terlebih terlihat termasuk ternyata tersampaikan
tersebut tersebutlah tertentu tertuju terus terutama tetap tetapi tiap tiba
tiba-tiba tidak tidakkah tidaklah tiga tinggi toh tunjuk turut tutur tuturnya
ucap ucapnya ujar ujarnya umum umumnya ungkap ungkapnya untuk usah usai
waduh wah wahai waktu waktunya walau walaupun wong
yaitu yakin yakni yang
""".split()
)
| 6,507 | 53.689076 | 79 | py |
spaCy | spaCy-master/spacy/lang/id/syntax_iterators.py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
# fmt: off
labels = ["nsubj", "nsubj:pass", "obj", "iobj", "ROOT", "appos", "nmod", "nmod:poss"]
# fmt: on
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_deps = [doc.vocab.strings[label] for label in labels]
conj = doc.vocab.strings.add("conj")
np_label = doc.vocab.strings.add("NP")
prev_end = -1
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.dep in np_deps:
prev_end = word.right_edge.i
yield word.left_edge.i, word.right_edge.i + 1, np_label
elif word.dep == conj:
head = word.head
while head.dep == conj and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
prev_end = word.right_edge.i
yield word.left_edge.i, word.right_edge.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
| 1,538 | 35.642857 | 89 | py |
spaCy | spaCy-master/spacy/lang/id/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ._tokenizer_exceptions_list import ID_BASE_EXCEPTIONS
# Daftar singkatan dan Akronim dari:
# https://id.wiktionary.org/wiki/Wiktionary:Daftar_singkatan_dan_akronim_bahasa_Indonesia#A
_exc = {}
for orth in ID_BASE_EXCEPTIONS:
_exc[orth] = [{ORTH: orth}]
orth_title = orth.title()
_exc[orth_title] = [{ORTH: orth_title}]
orth_caps = orth.upper()
_exc[orth_caps] = [{ORTH: orth_caps}]
orth_lower = orth.lower()
_exc[orth_lower] = [{ORTH: orth_lower}]
orth_first_upper = orth[0].upper() + orth[1:]
_exc[orth_first_upper] = [{ORTH: orth_first_upper}]
if "-" in orth:
orth_title = "-".join([part.title() for part in orth.split("-")])
_exc[orth_title] = [{ORTH: orth_title}]
orth_caps = "-".join([part.upper() for part in orth.split("-")])
_exc[orth_caps] = [{ORTH: orth_caps}]
for exc_data in [
{ORTH: "Jan.", NORM: "Januari"},
{ORTH: "Feb.", NORM: "Februari"},
{ORTH: "Mar.", NORM: "Maret"},
{ORTH: "Apr.", NORM: "April"},
{ORTH: "Jun.", NORM: "Juni"},
{ORTH: "Jul.", NORM: "Juli"},
{ORTH: "Agu.", NORM: "Agustus"},
{ORTH: "Ags.", NORM: "Agustus"},
{ORTH: "Sep.", NORM: "September"},
{ORTH: "Okt.", NORM: "Oktober"},
{ORTH: "Nov.", NORM: "November"},
{ORTH: "Des.", NORM: "Desember"},
]:
_exc[exc_data[ORTH]] = [exc_data]
_other_exc = {
"do'a": [{ORTH: "do'a", NORM: "doa"}],
"jum'at": [{ORTH: "jum'at", NORM: "Jumat"}],
"Jum'at": [{ORTH: "Jum'at", NORM: "Jumat"}],
"la'nat": [{ORTH: "la'nat", NORM: "laknat"}],
"ma'af": [{ORTH: "ma'af", NORM: "maaf"}],
"mu'jizat": [{ORTH: "mu'jizat", NORM: "mukjizat"}],
"Mu'jizat": [{ORTH: "Mu'jizat", NORM: "mukjizat"}],
"ni'mat": [{ORTH: "ni'mat", NORM: "nikmat"}],
"raka'at": [{ORTH: "raka'at", NORM: "rakaat"}],
"ta'at": [{ORTH: "ta'at", NORM: "taat"}],
}
_exc.update(_other_exc)
for orth in [
"A.AB.",
"A.Ma.",
"A.Md.",
"A.Md.Keb.",
"A.Md.Kep.",
"A.P.",
"B.A.",
"B.Ch.E.",
"B.Sc.",
"Dr.",
"Dra.",
"Drs.",
"Hj.",
"Ka.",
"Kp.",
"M.AB",
"M.Ag.",
"M.AP",
"M.Arl",
"M.A.R.S",
"M.Hum.",
"M.I.Kom.",
"M.Kes,",
"M.Kom.",
"M.M.",
"M.P.",
"M.Pd.",
"M.Psi.",
"M.Psi.T.",
"M.Sc.",
"M.SArl",
"M.Si.",
"M.Sn.",
"M.T.",
"M.Th.",
"No.",
"Pjs.",
"Plt.",
"R.A.",
"S.AB",
"S.AP",
"S.Adm",
"S.Ag.",
"S.Agr",
"S.Ant",
"S.Arl",
"S.Ars",
"S.A.R.S",
"S.Ds",
"S.E.",
"S.E.I.",
"S.Farm",
"S.Gz.",
"S.H.",
"S.Han",
"S.H.Int",
"S.Hum",
"S.Hut.",
"S.In.",
"S.IK.",
"S.I.Kom.",
"S.I.P",
"S.IP",
"S.P.",
"S.Pt",
"S.Psi",
"S.Ptk",
"S.Keb",
"S.Ked",
"S.Kep",
"S.KG",
"S.KH",
"S.Kel",
"S.K.M.",
"S.Kedg.",
"S.Kedh.",
"S.Kom.",
"S.KPM",
"S.Mb",
"S.Mat",
"S.Par",
"S.Pd.",
"S.Pd.I.",
"S.Pd.SD",
"S.Pol.",
"S.Psi.",
"S.S.",
"S.SArl.",
"S.Sn",
"S.Si.",
"S.Si.Teol.",
"S.SI.",
"S.ST.",
"S.ST.Han",
"S.STP",
"S.Sos.",
"S.Sy.",
"S.T.",
"S.T.Han",
"S.Th.",
"S.Th.I" "S.TI.",
"S.T.P.",
"S.TrK",
"S.Tekp.",
"S.Th.",
"Prof.",
"drg.",
"KH.",
"Ust.",
"Lc",
"Pdt.",
"S.H.H.",
"Rm.",
"Ps.",
"St.",
"M.A.",
"M.B.A",
"M.Eng.",
"M.Eng.Sc.",
"M.Pharm.",
"Dr. med",
"Dr.-Ing",
"Dr. rer. nat.",
"Dr. phil.",
"Dr. iur.",
"Dr. rer. oec",
"Dr. rer. pol.",
"R.Ng.",
"R.",
"R.M.",
"R.B.",
"R.P.",
"R.Ay.",
"Rr.",
"R.Ngt.",
"a.l.",
"a.n.",
"a.s.",
"b.d.",
"d.a.",
"d.l.",
"d/h",
"dkk.",
"dll.",
"dr.",
"drh.",
"ds.",
"dsb.",
"dst.",
"faks.",
"fax.",
"hlm.",
"i/o",
"n.b.",
"p.p." "pjs.",
"s.d.",
"tel.",
"u.p.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 4,204 | 18.027149 | 91 | py |
spaCy | spaCy-master/spacy/lang/is/__init__.py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class IcelandicDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Icelandic(Language):
lang = "is"
Defaults = IcelandicDefaults
__all__ = ["Icelandic"]
| 255 | 16.066667 | 46 | py |
spaCy | spaCy-master/spacy/lang/is/stop_words.py | # Source: https://github.com/Xangis/extra-stopwords
STOP_WORDS = set(
"""
afhverju
aftan
aftur
afþví
aldrei
allir
allt
alveg
annað
annars
bara
dag
eða
eftir
eiga
einhver
einhverjir
einhvers
eins
einu
eitthvað
ekkert
ekki
ennþá
eru
fara
fer
finna
fjöldi
fólk
framan
frá
frekar
fyrir
gegnum
geta
getur
gmg
gott
hann
hafa
hef
hefur
heyra
hér
hérna
hjá
hún
hvað
hvar
hver
hverjir
hverjum
hvernig
hvor
hvort
hægt
img
inn
kannski
koma
líka
lol
maður
mátt
mér
með
mega
meira
mig
mikið
minna
minni
missa
mjög
nei
niður
núna
oft
okkar
okkur
póst
póstur
rofl
saman
sem
sér
sig
sinni
síðan
sjá
smá
smátt
spurja
spyrja
staðar
stórt
svo
svona
sælir
sæll
taka
takk
til
tilvitnun
titlar
upp
var
vel
velkomin
velkominn
vera
verður
verið
vel
við
vil
vilja
vill
vita
væri
yfir
ykkar
það
þakka
þakkir
þannig
það
þar
þarf
þau
þeim
þeir
þeirra
þeirra
þegar
þess
þessa
þessi
þessu
þessum
þetta
þér
þið
þinn
þitt
þín
þráð
þráður
því
þær
ætti
""".split()
)
| 938 | 4.90566 | 51 | py |
spaCy | spaCy-master/spacy/lang/it/__init__.py | from typing import Callable, Optional
from thinc.api import Model
from ...language import BaseDefaults, Language
from .lemmatizer import ItalianLemmatizer
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class ItalianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
class Italian(Language):
lang = "it"
Defaults = ItalianDefaults
@Italian.factory(
"lemmatizer",
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "pos_lookup",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)
def make_lemmatizer(
nlp: Language,
model: Optional[Model],
name: str,
mode: str,
overwrite: bool,
scorer: Optional[Callable],
):
return ItalianLemmatizer(
nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
__all__ = ["Italian"]
| 1,230 | 23.137255 | 77 | py |
spaCy | spaCy-master/spacy/lang/it/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.it.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple vuole comprare una startup del Regno Unito per un miliardo di dollari",
"Le automobili a guida autonoma spostano la responsabilità assicurativa verso i produttori",
"San Francisco prevede di bandire i robot di consegna porta a porta",
"Londra è una grande città del Regno Unito.",
]
| 468 | 30.266667 | 96 | py |
spaCy | spaCy-master/spacy/lang/it/lemmatizer.py | from typing import Dict, List, Tuple
from ...pipeline import Lemmatizer
from ...tokens import Token
class ItalianLemmatizer(Lemmatizer):
"""This lemmatizer was adapted from the Polish one (version of April 2021).
It implements lookup lemmatization based on the morphological lexicon
morph-it (Baroni and Zanchetta). The table lemma_lookup with non-POS-aware
entries is used as a backup for words that aren't handled by morph-it."""
@classmethod
def get_lookups_config(cls, mode: str) -> Tuple[List[str], List[str]]:
if mode == "pos_lookup":
required = [
"lemma_lookup_num",
"lemma_lookup_det",
"lemma_lookup_adp",
"lemma_lookup_adj",
"lemma_lookup_noun",
"lemma_lookup_pron",
"lemma_lookup_verb",
"lemma_lookup_aux",
"lemma_lookup_adv",
"lemma_lookup_other",
"lemma_lookup",
]
return (required, [])
else:
return super().get_lookups_config(mode)
def pos_lookup_lemmatize(self, token: Token) -> List[str]:
string = token.text
univ_pos = token.pos_
morphology = token.morph.to_dict()
lookup_pos = univ_pos.lower()
if univ_pos == "PROPN":
lookup_pos = "noun"
elif univ_pos == "PART":
lookup_pos = "pron"
lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
if univ_pos == "NOUN":
return self.lemmatize_noun(string, morphology, lookup_table)
else:
if univ_pos != "PROPN":
string = string.lower()
if univ_pos == "DET":
return self.lemmatize_det(string, morphology, lookup_table)
elif univ_pos == "PRON":
return self.lemmatize_pron(string, morphology, lookup_table)
elif univ_pos == "ADP":
return self.lemmatize_adp(string, morphology, lookup_table)
elif univ_pos == "ADJ":
return self.lemmatize_adj(string, morphology, lookup_table)
else:
lemma = lookup_table.get(string, "")
if not lemma:
lookup_table = self.lookups.get_table("lemma_lookup_other")
lemma = lookup_table.get(string, "")
if not lemma:
lookup_table = self.lookups.get_table(
"lemma_lookup"
) # "legacy" lookup table
lemma = lookup_table.get(string, string.lower())
return [lemma]
def lemmatize_det(
self, string: str, morphology: dict, lookup_table: Dict[str, str]
) -> List[str]:
if string in [
"l'",
"lo",
"la",
"i",
"gli",
"le",
]:
return ["il"]
if string in ["un'", "un", "una"]:
return ["uno"]
return [lookup_table.get(string, string)]
def lemmatize_pron(
self, string: str, morphology: dict, lookup_table: Dict[str, str]
) -> List[str]:
if string in [
"l'",
"li",
"la",
"gli",
"le",
]:
return ["lo"]
if string in ["un'", "un", "una"]:
return ["uno"]
lemma = lookup_table.get(string, string)
if lemma == "alcun":
lemma = "alcuno"
elif lemma == "qualcun":
lemma = "qualcuno"
return [lemma]
def lemmatize_adp(
self, string: str, morphology: dict, lookup_table: Dict[str, str]
) -> List[str]:
if string == "d'":
return ["di"]
return [lookup_table.get(string, string)]
def lemmatize_adj(
self, string: str, morphology: dict, lookup_table: Dict[str, str]
) -> List[str]:
lemma = lookup_table.get(string, string)
if lemma == "alcun":
lemma = "alcuno"
elif lemma == "qualcun":
lemma = "qualcuno"
return [lemma]
def lemmatize_noun(
self, string: str, morphology: dict, lookup_table: Dict[str, str]
) -> List[str]:
# this method is case-sensitive, in order to work
# for incorrectly tagged proper names
if string != string.lower():
if string.lower() in lookup_table:
return [lookup_table[string.lower()]]
elif string in lookup_table:
return [lookup_table[string]]
return [string.lower()]
return [lookup_table.get(string, string)]
| 4,615 | 33.706767 | 79 | py |
spaCy | spaCy-master/spacy/lang/it/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
ELISION = "'’"
_prefixes = [r"'[0-9][0-9]", r"[0-9]+°"] + BASE_TOKENIZER_PREFIXES
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])(?:{h})(?=[{al}])".format(a=ALPHA, h=HYPHENS, al=ALPHA_LOWER),
r"(?<=[{a}0-9])[:<>=\/](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}][{el}])(?=[{a}0-9\"])".format(a=ALPHA, el=ELISION),
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_INFIXES = _infixes
| 850 | 23.314286 | 82 | py |
spaCy | spaCy-master/spacy/lang/it/stop_words.py | STOP_WORDS = set(
"""
a abbastanza abbia abbiamo abbiano abbiate accidenti ad adesso affinche agl
agli ahime ahimè ai al alcuna alcuni alcuno all alla alle allo allora altri
altrimenti altro altrove altrui anche ancora anni anno ansa anticipo assai
attesa attraverso avanti avemmo avendo avente aver avere averlo avesse
avessero avessi avessimo aveste avesti avete aveva avevamo avevano avevate
avevi avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste
avresti avrete avrà avrò avuta avute avuti avuto
basta bene benissimo brava bravo
casa caso cento certa certe certi certo che chi chicchessia chiunque ci c'
ciascuna ciascuno cima cio cioe circa citta città co codesta codesti codesto
cogli coi col colei coll coloro colui come cominci comunque con concernente
conciliarsi conclusione consiglio contro cortesia cos cosa cosi così cui
d' da dagl dagli dai dal dall dall' dalla dalle dallo dappertutto davanti degl degli
dei del dell dell' della delle dello dentro detto deve di dice dietro dire
dirimpetto diventa diventare diventato dopo dov dove dovra dovrà dovunque due
dunque durante
e ebbe ebbero ebbi ecc ecco ed effettivamente egli ella entrambi eppure era
erano eravamo eravate eri ero esempio esse essendo esser essere essi ex è
fa faccia facciamo facciano facciate faccio facemmo facendo facesse facessero
facessi facessimo faceste facesti faceva facevamo facevano facevate facevi
facevo fai fanno farai faranno fare farebbe farebbero farei faremmo faremo
fareste faresti farete farà farò fatto favore fece fecero feci fin finalmente
finche fine fino forse forza fosse fossero fossi fossimo foste fosti fra
frattempo fu fui fummo fuori furono futuro generale
gia già giacche giorni giorno gli gl' gliela gliele glieli glielo gliene governo
grande grazie gruppo
ha haha hai hanno ho
ieri il improvviso in inc infatti inoltre insieme intanto intorno invece io
l' la là lasciato lato lavoro le lei li lo lontano loro lui lungo luogo
m' ma macche magari maggior mai male malgrado malissimo mancanza marche me
medesimo mediante meglio meno mentre mesi mezzo mi mia mie miei mila miliardi
milioni minimi ministro mio modo molti moltissimo molto momento mondo mosto
nazionale ne negl negli nei nel nell nella nelle nello nemmeno neppure nessun nessun'
nessuna nessuno nient' niente no noi non nondimeno nonostante nonsia nostra nostre
nostri nostro novanta nove nulla nuovo
od oggi ogni ognuna ognuno oltre oppure ora ore osi ossia ottanta otto
paese parecchi parecchie parecchio parte partendo peccato peggio per perche
perché percio perciò perfino pero persino persone però piedi pieno piglia piu
piuttosto più po pochissimo poco poi poiche possa possedere posteriore posto
potrebbe preferibilmente presa press prima primo principalmente probabilmente
proprio puo può pure purtroppo
qualche qualcosa qualcuna qualcuno quale quali qualunque quando quanta quante
quanti quanto quantunque quasi quattro quel quel' quella quelle quelli quello quest quest'
questa queste questi questo qui quindi
realmente recente recentemente registrazione relativo riecco salvo
s' sara sarà sarai saranno sarebbe sarebbero sarei saremmo saremo sareste
saresti sarete saro sarò scola scopo scorso se secondo seguente seguito sei
sembra sembrare sembrato sembri sempre senza sette si sia siamo siano siate
siete sig solito solo soltanto sono sopra sotto spesso srl sta stai stando
stanno starai staranno starebbe starebbero starei staremmo staremo stareste
staresti starete starà starò stata state stati stato stava stavamo stavano
stavate stavi stavo stemmo stessa stesse stessero stessi stessimo stesso
steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua
subito successivamente successivo sue sugl sugli sui sul sull sulla sulle
sullo suo suoi
t' tale tali talvolta tanto te tempo ti titolo tra tranne tre trenta
troppo trovato tu tua tue tuo tuoi tutta tuttavia tutte tutti tutto
uguali ulteriore ultimo un un' una uno uomo
v' va vale vari varia varie vario verso vi via vicino visto vita voi volta volte
vostra vostre vostri vostro
""".split()
)
| 4,094 | 47.75 | 90 | py |
spaCy | spaCy-master/spacy/lang/it/syntax_iterators.py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import NOUN, PRON, PROPN
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
labels = [
"nsubj",
"nsubj:pass",
"obj",
"obl",
"obl:agent",
"nmod",
"pcomp",
"appos",
"ROOT",
]
post_modifiers = ["flat", "flat:name", "fixed", "compound"]
dets = ["det", "det:poss"]
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_deps = {doc.vocab.strings.add(label) for label in labels}
np_modifs = {doc.vocab.strings.add(modifier) for modifier in post_modifiers}
np_label = doc.vocab.strings.add("NP")
adj_label = doc.vocab.strings.add("amod")
det_labels = {doc.vocab.strings.add(det) for det in dets}
det_pos = doc.vocab.strings.add("DET")
adp_label = doc.vocab.strings.add("ADP")
conj = doc.vocab.strings.add("conj")
conj_pos = doc.vocab.strings.add("CCONJ")
prev_end = -1
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.dep in np_deps:
right_childs = list(word.rights)
right_child = right_childs[0] if right_childs else None
if right_child:
if (
right_child.dep == adj_label
): # allow chain of adjectives by expanding to right
right_end = right_child.right_edge
elif (
right_child.dep in det_labels and right_child.pos == det_pos
): # cut relative pronouns here
right_end = right_child
elif right_child.dep in np_modifs: # Check if we can expand to right
right_end = word.right_edge
else:
right_end = word
else:
right_end = word
prev_end = right_end.i
left_index = word.left_edge.i
left_index = (
left_index + 1 if word.left_edge.pos == adp_label else left_index
)
yield left_index, right_end.i + 1, np_label
elif word.dep == conj:
head = word.head
while head.dep == conj and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
prev_end = word.i
left_index = word.left_edge.i # eliminate left attached conjunction
left_index = (
left_index + 1 if word.left_edge.pos == conj_pos else left_index
)
yield left_index, word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
| 3,137 | 35.068966 | 85 | py |
spaCy | spaCy-master/spacy/lang/it/tokenizer_exceptions.py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {
"all'art.": [{ORTH: "all'"}, {ORTH: "art."}],
"dall'art.": [{ORTH: "dall'"}, {ORTH: "art."}],
"dell'art.": [{ORTH: "dell'"}, {ORTH: "art."}],
"L'art.": [{ORTH: "L'"}, {ORTH: "art."}],
"l'art.": [{ORTH: "l'"}, {ORTH: "art."}],
"nell'art.": [{ORTH: "nell'"}, {ORTH: "art."}],
"po'": [{ORTH: "po'"}],
"sett..": [{ORTH: "sett."}, {ORTH: "."}],
}
for orth in [
"..",
"....",
"a.C.",
"al.",
"all-path",
"art.",
"Art.",
"artt.",
"att.",
"avv.",
"Avv.",
"by-pass",
"c.d.",
"c/c",
"C.so",
"centro-sinistra",
"check-up",
"Civ.",
"cm.",
"Cod.",
"col.",
"Cost.",
"d.C.",
'de"',
"distr.",
"E'",
"ecc.",
"e-mail",
"e/o",
"etc.",
"Jr.",
"n°",
"nord-est",
"pag.",
"Proc.",
"prof.",
"sett.",
"s.p.a.",
"s.n.c",
"s.r.l",
"ss.",
"St.",
"tel.",
"week-end",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 1,159 | 16.846154 | 56 | py |
spaCy | spaCy-master/spacy/lang/ja/__init__.py | import re
from collections import namedtuple
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Union
import srsly
from thinc.api import Model
from ... import util
from ...errors import Errors
from ...language import BaseDefaults, Language
from ...pipeline import Morphologizer
from ...pipeline.morphologizer import DEFAULT_MORPH_MODEL
from ...scorer import Scorer
from ...symbols import POS
from ...tokens import Doc, MorphAnalysis
from ...training import validate_examples
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tag_bigram_map import TAG_BIGRAM_MAP
from .tag_map import TAG_MAP
from .tag_orth_map import TAG_ORTH_MAP
DEFAULT_CONFIG = """
[nlp]
[nlp.tokenizer]
@tokenizers = "spacy.ja.JapaneseTokenizer"
split_mode = null
"""
@registry.tokenizers("spacy.ja.JapaneseTokenizer")
def create_tokenizer(split_mode: Optional[str] = None):
def japanese_tokenizer_factory(nlp):
return JapaneseTokenizer(nlp.vocab, split_mode=split_mode)
return japanese_tokenizer_factory
class JapaneseTokenizer(DummyTokenizer):
def __init__(self, vocab: Vocab, split_mode: Optional[str] = None) -> None:
self.vocab = vocab
self.split_mode = split_mode
self.tokenizer = try_sudachi_import(self.split_mode)
# if we're using split mode A we don't need subtokens
self.need_subtokens = not (split_mode is None or split_mode == "A")
def __reduce__(self):
return JapaneseTokenizer, (self.vocab, self.split_mode)
def __call__(self, text: str) -> Doc:
# convert sudachipy.morpheme.Morpheme to DetailedToken and merge continuous spaces
sudachipy_tokens = self.tokenizer.tokenize(text)
dtokens = self._get_dtokens(sudachipy_tokens)
dtokens, spaces = get_dtokens_and_spaces(dtokens, text)
# create Doc with tag bi-gram based part-of-speech identification rules
words, tags, inflections, lemmas, norms, readings, sub_tokens_list = (
zip(*dtokens) if dtokens else [[]] * 7
)
sub_tokens_list = list(sub_tokens_list)
doc = Doc(self.vocab, words=words, spaces=spaces)
next_pos = None # for bi-gram rules
for idx, (token, dtoken) in enumerate(zip(doc, dtokens)):
token.tag_ = dtoken.tag
if next_pos: # already identified in previous iteration
token.pos = next_pos
next_pos = None
else:
token.pos, next_pos = resolve_pos(
token.orth_,
dtoken.tag,
tags[idx + 1] if idx + 1 < len(tags) else None,
)
# if there's no lemma info (it's an unk) just use the surface
token.lemma_ = dtoken.lemma if dtoken.lemma else dtoken.surface
morph = {}
if dtoken.inf:
# it's normal for this to be empty for non-inflecting types
morph["Inflection"] = dtoken.inf
token.norm_ = dtoken.norm
if dtoken.reading:
# punctuation is its own reading, but we don't want values like
# "=" here
morph["Reading"] = re.sub("[=|]", "_", dtoken.reading)
token.morph = MorphAnalysis(self.vocab, morph)
if self.need_subtokens:
doc.user_data["sub_tokens"] = sub_tokens_list
return doc
def _get_dtokens(self, sudachipy_tokens, need_sub_tokens: bool = True):
sub_tokens_list = (
self._get_sub_tokens(sudachipy_tokens) if need_sub_tokens else None
)
dtokens = [
DetailedToken(
token.surface(), # orth
"-".join([xx for xx in token.part_of_speech()[:4] if xx != "*"]), # tag
";".join([xx for xx in token.part_of_speech()[4:] if xx != "*"]), # inf
token.dictionary_form(), # lemma
token.normalized_form(),
token.reading_form(),
sub_tokens_list[idx]
if sub_tokens_list
else None, # user_data['sub_tokens']
)
for idx, token in enumerate(sudachipy_tokens)
if len(token.surface()) > 0
# remove empty tokens which can be produced with characters like … that
]
# Sudachi normalizes internally and outputs each space char as a token.
# This is the preparation for get_dtokens_and_spaces() to merge the continuous space tokens
return [
t
for idx, t in enumerate(dtokens)
if idx == 0
or not t.surface.isspace()
or t.tag != "空白"
or not dtokens[idx - 1].surface.isspace()
or dtokens[idx - 1].tag != "空白"
]
def _get_sub_tokens(self, sudachipy_tokens):
# do nothing for default split mode
if not self.need_subtokens:
return None
sub_tokens_list = [] # list of (list of list of DetailedToken | None)
for token in sudachipy_tokens:
sub_a = token.split(self.tokenizer.SplitMode.A)
if len(sub_a) == 1: # no sub tokens
sub_tokens_list.append(None)
elif self.split_mode == "B":
sub_tokens_list.append([self._get_dtokens(sub_a, False)])
else: # "C"
sub_b = token.split(self.tokenizer.SplitMode.B)
if len(sub_a) == len(sub_b):
dtokens = self._get_dtokens(sub_a, False)
sub_tokens_list.append([dtokens, dtokens])
else:
sub_tokens_list.append(
[
self._get_dtokens(sub_a, False),
self._get_dtokens(sub_b, False),
]
)
return sub_tokens_list
def score(self, examples):
validate_examples(examples, "JapaneseTokenizer.score")
return Scorer.score_tokenization(examples)
def _get_config(self) -> Dict[str, Any]:
return {"split_mode": self.split_mode}
def _set_config(self, config: Dict[str, Any] = {}) -> None:
self.split_mode = config.get("split_mode", None)
def to_bytes(self, **kwargs) -> bytes:
serializers = {"cfg": lambda: srsly.json_dumps(self._get_config())}
return util.to_bytes(serializers, [])
def from_bytes(self, data: bytes, **kwargs) -> "JapaneseTokenizer":
deserializers = {"cfg": lambda b: self._set_config(srsly.json_loads(b))}
util.from_bytes(data, deserializers, [])
self.tokenizer = try_sudachi_import(self.split_mode)
return self
def to_disk(self, path: Union[str, Path], **kwargs) -> None:
path = util.ensure_path(path)
serializers = {"cfg": lambda p: srsly.write_json(p, self._get_config())}
util.to_disk(path, serializers, [])
def from_disk(self, path: Union[str, Path], **kwargs) -> "JapaneseTokenizer":
path = util.ensure_path(path)
serializers = {"cfg": lambda p: self._set_config(srsly.read_json(p))}
util.from_disk(path, serializers, [])
self.tokenizer = try_sudachi_import(self.split_mode)
return self
class JapaneseDefaults(BaseDefaults):
config = load_config_from_str(DEFAULT_CONFIG)
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
class Japanese(Language):
lang = "ja"
Defaults = JapaneseDefaults
@Japanese.factory(
"morphologizer",
assigns=["token.morph", "token.pos"],
default_config={
"model": DEFAULT_MORPH_MODEL,
"overwrite": True,
"extend": True,
"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
},
default_score_weights={
"pos_acc": 0.5,
"morph_micro_f": 0.5,
"morph_per_feat": None,
},
)
def make_morphologizer(
nlp: Language,
model: Model,
name: str,
overwrite: bool,
extend: bool,
scorer: Optional[Callable],
):
return Morphologizer(
nlp.vocab, model, name, overwrite=overwrite, extend=extend, scorer=scorer
)
# Hold the attributes we need with convenient names
DetailedToken = namedtuple(
"DetailedToken", ["surface", "tag", "inf", "lemma", "norm", "reading", "sub_tokens"]
)
def try_sudachi_import(split_mode="A"):
"""SudachiPy is required for Japanese support, so check for it.
It it's not available blow up and explain how to fix it.
split_mode should be one of these values: "A", "B", "C", None->"A"."""
try:
from sudachipy import dictionary, tokenizer
split_mode = {
None: tokenizer.Tokenizer.SplitMode.A,
"A": tokenizer.Tokenizer.SplitMode.A,
"B": tokenizer.Tokenizer.SplitMode.B,
"C": tokenizer.Tokenizer.SplitMode.C,
}[split_mode]
tok = dictionary.Dictionary().create(mode=split_mode)
return tok
except ImportError:
raise ImportError(
"Japanese support requires SudachiPy and SudachiDict-core "
"(https://github.com/WorksApplications/SudachiPy). "
"Install with `pip install sudachipy sudachidict_core` or "
"install spaCy with `pip install spacy[ja]`."
) from None
def resolve_pos(orth, tag, next_tag):
"""If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
in the sentence. This function returns resolved POSs for both token
and next_token by tuple.
"""
# Some tokens have their UD tag decided based on the POS of the following
# token.
# apply orth based mapping
if tag in TAG_ORTH_MAP:
orth_map = TAG_ORTH_MAP[tag]
if orth in orth_map:
return orth_map[orth], None # current_pos, next_pos
# apply tag bi-gram mapping
if next_tag:
tag_bigram = tag, next_tag
if tag_bigram in TAG_BIGRAM_MAP:
current_pos, next_pos = TAG_BIGRAM_MAP[tag_bigram]
if current_pos is None: # apply tag uni-gram mapping for current_pos
return (
TAG_MAP[tag][POS],
next_pos,
) # only next_pos is identified by tag bi-gram mapping
else:
return current_pos, next_pos
# apply tag uni-gram mapping
return TAG_MAP[tag][POS], None
def get_dtokens_and_spaces(dtokens, text, gap_tag="空白"):
# Compare the content of tokens and text, first
words = [x.surface for x in dtokens]
if "".join("".join(words).split()) != "".join(text.split()):
raise ValueError(Errors.E194.format(text=text, words=words))
text_dtokens = []
text_spaces = []
text_pos = 0
# handle empty and whitespace-only texts
if len(words) == 0:
return text_dtokens, text_spaces
elif len([word for word in words if not word.isspace()]) == 0:
assert text.isspace()
text_dtokens = [DetailedToken(text, gap_tag, "", text, text, None, None)]
text_spaces = [False]
return text_dtokens, text_spaces
# align words and dtokens by referring text, and insert gap tokens for the space char spans
for i, (word, dtoken) in enumerate(zip(words, dtokens)):
# skip all space tokens
if word.isspace():
continue
try:
word_start = text[text_pos:].index(word)
except ValueError:
raise ValueError(Errors.E194.format(text=text, words=words)) from None
# space token
if word_start > 0:
w = text[text_pos : text_pos + word_start]
text_dtokens.append(DetailedToken(w, gap_tag, "", w, w, None, None))
text_spaces.append(False)
text_pos += word_start
# content word
text_dtokens.append(dtoken)
text_spaces.append(False)
text_pos += len(word)
# poll a space char after the word
if i + 1 < len(dtokens) and dtokens[i + 1].surface == " ":
text_spaces[-1] = True
text_pos += 1
# trailing space token
if text_pos < len(text):
w = text[text_pos:]
text_dtokens.append(DetailedToken(w, gap_tag, "", w, w, None, None))
text_spaces.append(False)
return text_dtokens, text_spaces
__all__ = ["Japanese"]
| 12,609 | 35.763848 | 99 | py |
spaCy | spaCy-master/spacy/lang/ja/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ja.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"アップルがイギリスの新興企業を１０億ドルで購入を検討",
"自動運転車の損害賠償責任、自動車メーカーに一定の負担を求める",
"歩道を走る自動配達ロボ、サンフランシスコ市が走行禁止を検討",
"ロンドンはイギリスの大都市です。",
]
| 297 | 18.866667 | 56 | py |
spaCy | spaCy-master/spacy/lang/ja/stop_words.py | # This list was created by taking the top 2000 words from a Wikipedia dump and
# filtering out everything that wasn't hiragana. ー (one) was also added.
# Considered keeping some non-hiragana words but too many place names were
# present.
STOP_WORDS = set(
"""
あ あっ あまり あり ある あるいは あれ
い いい いう いく いずれ いっ いつ いる いわ
うち
え
お おい おけ および おら おり
か かけ かつ かつて かなり から が
き きっかけ
くる くん
こ こう ここ こと この これ ご ごと
さ さらに さん
し しか しかし しまう しまっ しよう
す すぐ すべて する ず
せ せい せる
そう そこ そして その それ それぞれ
た たい ただし たち ため たら たり だ だけ だっ
ち ちゃん
つ つい つけ つつ
て で でき できる です
と とき ところ とっ とも どう
な ない なお なかっ ながら なく なけれ なし なっ など なら なり なる
に にて
ぬ
ね
の のち のみ
は はじめ ば
ひと
ぶり
へ べき
ほか ほとんど ほど ほぼ
ま ます また まで まま
み
も もう もっ もと もの
や やっ
よ よう よく よっ より よる よれ
ら らしい られ られる
る
れ れる
を
ん
一
""".split()
)
| 730 | 13.918367 | 78 | py |
spaCy | spaCy-master/spacy/lang/ja/syntax_iterators.py | from typing import Iterator, Set, Tuple, Union
from ...symbols import NOUN, PRON, PROPN, VERB
from ...tokens import Doc, Span
# TODO: this can probably be pruned a bit
# fmt: off
labels = ["nsubj", "nmod", "ddoclike", "nsubjpass", "pcomp", "pdoclike", "doclike", "obl", "dative", "appos", "attr", "ROOT"]
# fmt: on
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
doc = doclike.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings.add(label) for label in labels]
doc.vocab.strings.add("conj")
np_label = doc.vocab.strings.add("NP")
seen: Set[int] = set()
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
unseen = [w.i for w in word.subtree if w.i not in seen]
if not unseen:
continue
# this takes care of particles etc.
seen.update(j.i for j in word.subtree)
# This avoids duplicating embedded clauses
seen.update(range(word.i + 1))
# if the head of this is a verb, mark that and rights seen
# Don't do the subtree as that can hide other phrases
if word.head.pos == VERB:
seen.add(word.head.i)
seen.update(w.i for w in word.head.rights)
yield unseen[0], word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
| 1,638 | 38.02381 | 125 | py |
spaCy | spaCy-master/spacy/lang/ja/tag_bigram_map.py | from ...symbols import ADJ, AUX, NOUN, PART, VERB
# mapping from tag bi-gram to pos of previous token
TAG_BIGRAM_MAP = {
# This covers only small part of AUX.
("形容詞-非自立可能", "助詞-終助詞"): (AUX, None),
("名詞-普通名詞-形状詞可能", "助動詞"): (ADJ, None),
# ("副詞", "名詞-普通名詞-形状詞可能"): (None, ADJ),
# This covers acl, advcl, obl and root, but has side effect for compound.
("名詞-普通名詞-サ変可能", "動詞-非自立可能"): (VERB, AUX),
# This covers almost all of the deps
("名詞-普通名詞-サ変形状詞可能", "動詞-非自立可能"): (VERB, AUX),
("名詞-普通名詞-副詞可能", "動詞-非自立可能"): (None, VERB),
("副詞", "動詞-非自立可能"): (None, VERB),
("形容詞-一般", "動詞-非自立可能"): (None, VERB),
("形容詞-非自立可能", "動詞-非自立可能"): (None, VERB),
("接頭辞", "動詞-非自立可能"): (None, VERB),
("助詞-係助詞", "動詞-非自立可能"): (None, VERB),
("助詞-副助詞", "動詞-非自立可能"): (None, VERB),
("助詞-格助詞", "動詞-非自立可能"): (None, VERB),
("補助記号-読点", "動詞-非自立可能"): (None, VERB),
("形容詞-一般", "接尾辞-名詞的-一般"): (None, PART),
("助詞-格助詞", "形状詞-助動詞語幹"): (None, NOUN),
("連体詞", "形状詞-助動詞語幹"): (None, NOUN),
("動詞-一般", "助詞-副助詞"): (None, PART),
("動詞-非自立可能", "助詞-副助詞"): (None, PART),
("助動詞", "助詞-副助詞"): (None, PART),
}
| 1,137 | 38.241379 | 77 | py |
spaCy | spaCy-master/spacy/lang/ja/tag_map.py | from ...symbols import (
ADJ,
ADP,
ADV,
AUX,
CCONJ,
DET,
INTJ,
NOUN,
NUM,
PART,
POS,
PRON,
PROPN,
PUNCT,
SCONJ,
SPACE,
SYM,
VERB,
)
TAG_MAP = {
# Explanation of Unidic tags:
# https://www.gavo.t.u-tokyo.ac.jp/~mine/japanese/nlp+slp/UNIDIC_manual.pdf
# Universal Dependencies Mapping: (Some of the entries in this mapping are updated to v2.6 in the list below)
# http://universaldependencies.org/ja/overview/morphology.html
# http://universaldependencies.org/ja/pos/all.html
"記号-一般": {POS: NOUN}, # this includes characters used to represent sounds like ドレミ
"記号-文字": {
POS: NOUN
}, # this is for Greek and Latin characters having some meanings, or used as symbols, as in math
"感動詞-フィラー": {POS: INTJ},
"感動詞-一般": {POS: INTJ},
"空白": {POS: SPACE},
"形状詞-一般": {POS: ADJ},
"形状詞-タリ": {POS: ADJ},
"形状詞-助動詞語幹": {POS: AUX},
"形容詞-一般": {POS: ADJ},
"形容詞-非自立可能": {POS: ADJ}, # XXX ADJ if alone, AUX otherwise
"助詞-格助詞": {POS: ADP},
"助詞-係助詞": {POS: ADP},
"助詞-終助詞": {POS: PART},
"助詞-準体助詞": {POS: SCONJ}, # の as in 走るのが速い
"助詞-接続助詞": {POS: SCONJ}, # verb ending て0
"助詞-副助詞": {POS: ADP}, # ばかり, つつ after a verb
"助動詞": {POS: AUX},
"接続詞": {POS: CCONJ}, # XXX: might need refinement
"接頭辞": {POS: NOUN},
"接尾辞-形状詞的": {POS: PART}, # がち, チック
"接尾辞-形容詞的": {POS: AUX}, # -らしい
"接尾辞-動詞的": {POS: PART}, # -じみ
"接尾辞-名詞的-サ変可能": {POS: NOUN}, # XXX see 名詞,普通名詞,サ変可能,*
"接尾辞-名詞的-一般": {POS: NOUN},
"接尾辞-名詞的-助数詞": {POS: NOUN},
"接尾辞-名詞的-副詞可能": {POS: NOUN}, # -後, -過ぎ
"代名詞": {POS: PRON},
"動詞-一般": {POS: VERB},
"動詞-非自立可能": {POS: AUX}, # XXX VERB if alone, AUX otherwise
"副詞": {POS: ADV},
"補助記号-ＡＡ-一般": {POS: SYM}, # text art
"補助記号-ＡＡ-顔文字": {POS: PUNCT}, # kaomoji
"補助記号-一般": {POS: SYM},
"補助記号-括弧開": {POS: PUNCT}, # open bracket
"補助記号-括弧閉": {POS: PUNCT}, # close bracket
"補助記号-句点": {POS: PUNCT}, # period or other EOS marker
"補助記号-読点": {POS: PUNCT}, # comma
"名詞-固有名詞-一般": {POS: PROPN}, # general proper noun
"名詞-固有名詞-人名-一般": {POS: PROPN}, # person's name
"名詞-固有名詞-人名-姓": {POS: PROPN}, # surname
"名詞-固有名詞-人名-名": {POS: PROPN}, # first name
"名詞-固有名詞-地名-一般": {POS: PROPN}, # place name
"名詞-固有名詞-地名-国": {POS: PROPN}, # country name
"名詞-助動詞語幹": {POS: AUX},
"名詞-数詞": {POS: NUM}, # includes Chinese numerals
"名詞-普通名詞-サ変可能": {POS: NOUN}, # XXX: sometimes VERB in UDv2; suru-verb noun
"名詞-普通名詞-サ変形状詞可能": {POS: NOUN},
"名詞-普通名詞-一般": {POS: NOUN},
"名詞-普通名詞-形状詞可能": {POS: NOUN}, # XXX: sometimes ADJ in UDv2
"名詞-普通名詞-助数詞可能": {POS: NOUN}, # counter / unit
"名詞-普通名詞-副詞可能": {POS: NOUN},
"連体詞": {POS: DET}, # XXX this has exceptions based on literal token
# GSD tags. These aren't in Unidic, but we need them for the GSD data.
"外国語": {POS: PROPN}, # Foreign words
"絵文字・記号等": {POS: SYM}, # emoji / kaomoji ^^;
}
| 3,001 | 33.906977 | 113 | py |
spaCy | spaCy-master/spacy/lang/ja/tag_orth_map.py | from ...symbols import DET, PART, PRON, SPACE, X
# mapping from tag bi-gram to pos of previous token
TAG_ORTH_MAP = {
"空白": {" ": SPACE, " ": X},
"助詞-副助詞": {"たり": PART},
"連体詞": {
"あの": DET,
"かの": DET,
"この": DET,
"その": DET,
"どの": DET,
"彼の": DET,
"此の": DET,
"其の": DET,
"ある": PRON,
"こんな": PRON,
"そんな": PRON,
"どんな": PRON,
"あらゆる": PRON,
},
}
| 458 | 18.956522 | 51 | py |
spaCy | spaCy-master/spacy/lang/kn/__init__.py | from ...language import BaseDefaults, Language
from .stop_words import STOP_WORDS
class KannadaDefaults(BaseDefaults):
stop_words = STOP_WORDS
class Kannada(Language):
lang = "kn"
Defaults = KannadaDefaults
__all__ = ["Kannada"]
| 247 | 15.533333 | 46 | py |
spaCy | spaCy-master/spacy/lang/kn/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.en.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"ಆಪಲ್ ಒಂದು ಯು.ಕೆ. ಸ್ಟಾರ್ಟ್ಅಪ್ ಅನ್ನು ೧ ಶತಕೋಟಿ ಡಾಲರ್ಗಳಿಗೆ ಖರೀದಿಸಲು ನೋಡುತ್ತಿದೆ.",
"ಸ್ವಾಯತ್ತ ಕಾರುಗಳು ವಿಮಾ ಹೊಣೆಗಾರಿಕೆಯನ್ನು ತಯಾರಕರ ಕಡೆಗೆ ಬದಲಾಯಿಸುತ್ತವೆ.",
"ಕಾಲುದಾರಿ ವಿತರಣಾ ರೋಬೋಟ್ಗಳನ್ನು ನಿಷೇಧಿಸುವುದನ್ನು ಸ್ಯಾನ್ ಫ್ರಾನ್ಸಿಸ್ಕೊ ಪರಿಗಣಿಸುತ್ತದೆ.",
"ಲಂಡನ್ ಯುನೈಟೆಡ್ ಕಿಂಗ್ಡಂನ ದೊಡ್ಡ ನಗರ.",
"ನೀನು ಎಲ್ಲಿದಿಯಾ?",
"ಫ್ರಾನ್ಸಾದ ಅಧ್ಯಕ್ಷರು ಯಾರು?",
"ಯುನೈಟೆಡ್ ಸ್ಟೇಟ್ಸ್ನ ರಾಜಧಾನಿ ಯಾವುದು?",
"ಬರಾಕ್ ಒಬಾಮ ಯಾವಾಗ ಜನಿಸಿದರು?",
]
| 585 | 29.842105 | 89 | py |
spaCy | spaCy-master/spacy/lang/kn/stop_words.py | STOP_WORDS = set(
"""
ಹಲವು
ಮೂಲಕ
ಹಾಗೂ
ಅದು
ನೀಡಿದ್ದಾರೆ
ಯಾವ
ಎಂದರು
ಅವರು
ಈಗ
ಎಂಬ
ಹಾಗಾಗಿ
ಅಷ್ಟೇ
ನಾವು
ಇದೇ
ಹೇಳಿ
ತಮ್ಮ
ಹೀಗೆ
ನಮ್ಮ
ಬೇರೆ
ನೀಡಿದರು
ಮತ್ತೆ
ಇದು
ಈ
ನೀವು
ನಾನು
ಇತ್ತು
ಎಲ್ಲಾ
ಯಾವುದೇ
ನಡೆದ
ಅದನ್ನು
ಎಂದರೆ
ನೀಡಿದೆ
ಹೀಗಾಗಿ
ಜೊತೆಗೆ
ಇದರಿಂದ
ನನಗೆ
ಅಲ್ಲದೆ
ಎಷ್ಟು
ಇದರ
ಇಲ್ಲ
ಕಳೆದ
ತುಂಬಾ
ಈಗಾಗಲೇ
ಮಾಡಿ
ಅದಕ್ಕೆ
ಬಗ್ಗೆ
ಅವರ
ಇದನ್ನು
ಆ
ಇದೆ
ಹೆಚ್ಚು
ಇನ್ನು
ಎಲ್ಲ
ಇರುವ
ಅವರಿಗೆ
ನಿಮ್ಮ
ಏನು
ಕೂಡ
ಇಲ್ಲಿ
ನನ್ನನ್ನು
ಕೆಲವು
ಮಾತ್ರ
ಬಳಿಕ
ಅಂತ
ತನ್ನ
ಆಗ
ಅಥವಾ
ಅಲ್ಲ
ಕೇವಲ
ಆದರೆ
ಮತ್ತು
ಇನ್ನೂ
ಅದೇ
ಆಗಿ
ಅವರನ್ನು
ಹೇಳಿದ್ದಾರೆ
ನಡೆದಿದೆ
ಇದಕ್ಕೆ
ಎಂಬುದು
ಎಂದು
ನನ್ನ
ಮೇಲೆ
""".split()
)
| 499 | 4.747126 | 17 | py |
spaCy | spaCy-master/spacy/lang/ko/__init__.py | from typing import Any, Dict, Iterator
from ...language import BaseDefaults, Language
from ...scorer import Scorer
from ...symbols import POS, X
from ...tokens import Doc
from ...training import validate_examples
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
DEFAULT_CONFIG = """
[nlp]
[nlp.tokenizer]
@tokenizers = "spacy.ko.KoreanTokenizer"
"""
@registry.tokenizers("spacy.ko.KoreanTokenizer")
def create_tokenizer():
def korean_tokenizer_factory(nlp):
return KoreanTokenizer(nlp.vocab)
return korean_tokenizer_factory
class KoreanTokenizer(DummyTokenizer):
def __init__(self, vocab: Vocab):
self.vocab = vocab
self._mecab = try_mecab_import() # type: ignore[func-returns-value]
self._mecab_tokenizer = None
@property
def mecab_tokenizer(self):
# This is a property so that initializing a pipeline with blank:ko is
# possible without actually requiring mecab-ko, e.g. to run
# `spacy init vectors ko` for a pipeline that will have a different
# tokenizer in the end. The languages need to match for the vectors
# to be imported and there's no way to pass a custom config to
# `init vectors`.
if self._mecab_tokenizer is None:
self._mecab_tokenizer = self._mecab("-F%f[0],%f[7]")
return self._mecab_tokenizer
def __reduce__(self):
return KoreanTokenizer, (self.vocab,)
def __call__(self, text: str) -> Doc:
dtokens = list(self.detailed_tokens(text))
surfaces = [dt["surface"] for dt in dtokens]
doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
for token, dtoken in zip(doc, dtokens):
first_tag, sep, eomi_tags = dtoken["tag"].partition("+")
token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
if token.tag_ in TAG_MAP:
token.pos = TAG_MAP[token.tag_][POS]
else:
token.pos = X
token.lemma_ = dtoken["lemma"]
doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
return doc
def detailed_tokens(self, text: str) -> Iterator[Dict[str, Any]]:
# 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
# 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
for node in self.mecab_tokenizer.parse(text, as_nodes=True):
if node.is_eos():
break
surface = node.surface
feature = node.feature
tag, _, expr = feature.partition(",")
lemma, _, remainder = expr.partition("/")
if lemma == "*":
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
def score(self, examples):
validate_examples(examples, "KoreanTokenizer.score")
return Scorer.score_tokenization(examples)
class KoreanDefaults(BaseDefaults):
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
infixes = TOKENIZER_INFIXES
class Korean(Language):
lang = "ko"
Defaults = KoreanDefaults
def try_mecab_import() -> None:
try:
from natto import MeCab
return MeCab
except ImportError:
raise ImportError(
'The Korean tokenizer ("spacy.ko.KoreanTokenizer") requires '
"[mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
"and [natto-py](https://github.com/buruzaemon/natto-py)"
) from None
def check_spaces(text, tokens):
prev_end = -1
start = 0
for token in tokens:
idx = text.find(token, start)
if prev_end > 0:
yield prev_end != idx
prev_end = idx + len(token)
start = prev_end
if start > 0:
yield False
__all__ = ["Korean"]
| 4,230 | 32.314961 | 88 | py |
spaCy | spaCy-master/spacy/lang/ko/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ko.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"애플이 영국의 스타트업을 10억 달러에 인수하는 것을 알아보고 있다.",
"자율주행 자동차의 손해 배상 책임이 제조 업체로 옮겨 가다",
"샌프란시스코 시가 자동 배달 로봇의 보도 주행 금지를 검토 중이라고 합니다.",
"런던은 영국의 수도이자 가장 큰 도시입니다.",
]
| 331 | 22.714286 | 56 | py |
spaCy | spaCy-master/spacy/lang/ko/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"영",
"공",
# Native Korean number system
"하나",
"둘",
"셋",
"넷",
"다섯",
"여섯",
"일곱",
"여덟",
"아홉",
"열",
"스물",
"서른",
"마흔",
"쉰",
"예순",
"일흔",
"여든",
"아흔",
# Sino-Korean number system
"일",
"이",
"삼",
"사",
"오",
"육",
"칠",
"팔",
"구",
"십",
"백",
"천",
"만",
"십만",
"백만",
"천만",
"일억",
"십억",
"백억",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if any(char.lower() in _num_words for char in text):
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 934 | 13.609375 | 56 | py |
spaCy | spaCy-master/spacy/lang/ko/punctuation.py | from ..char_classes import LIST_QUOTES
from ..punctuation import TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES
_infixes = (
["·", "ㆍ", r"\(", r"\)"]
+ [r"(?<=[0-9])~(?=[0-9-])"]
+ LIST_QUOTES
+ BASE_TOKENIZER_INFIXES
)
TOKENIZER_INFIXES = _infixes
| 264 | 21.083333 | 69 | py |
spaCy | spaCy-master/spacy/lang/ko/stop_words.py | STOP_WORDS = set(
"""
이
있
하
것
들
그
되
수
이
보
않
없
나
주
아니
등
같
때
년
가
한
지
오
말
일
그렇
위하
때문
그것
두
말하
알
그러나
받
못하
일
그런
또
더
많
그리고
좋
크
시키
그러
하나
살
데
안
어떤
번
나
다른
어떻
들
이렇
점
싶
말
좀
원
잘
놓
""".split()
)
| 185 | 1.735294 | 17 | py |
spaCy | spaCy-master/spacy/lang/ko/tag_map.py | from ...symbols import (
ADJ,
ADP,
ADV,
AUX,
CONJ,
DET,
INTJ,
NOUN,
NUM,
POS,
PRON,
PROPN,
PUNCT,
SYM,
VERB,
X,
)
# 은전한닢(mecab-ko-dic)의 품사 태그를 universal pos tag로 대응시킴
# https://docs.google.com/spreadsheets/d/1-9blXKjtjeKZqsf4NzHeYJCrr49-nXeRF6D80udfcwY/edit#gid=589544265
# https://universaldependencies.org/u/pos/
TAG_MAP = {
# J.{1,2} 조사
"JKS": {POS: ADP},
"JKC": {POS: ADP},
"JKG": {POS: ADP},
"JKO": {POS: ADP},
"JKB": {POS: ADP},
"JKV": {POS: ADP},
"JKQ": {POS: ADP},
"JX": {POS: ADP}, # 보조사
"JC": {POS: CONJ}, # 접속 조사
"MAJ": {POS: CONJ}, # 접속 부사
"MAG": {POS: ADV}, # 일반 부사
"MM": {POS: DET}, # 관형사
"XPN": {POS: X}, # 접두사
# XS. 접미사
"XSN": {POS: X},
"XSV": {POS: X},
"XSA": {POS: X},
"XR": {POS: X}, # 어근
# E.{1,2} 어미
"EP": {POS: X},
"EF": {POS: X},
"EC": {POS: X},
"ETN": {POS: X},
"ETM": {POS: X},
"IC": {POS: INTJ}, # 감탄사
"VV": {POS: VERB}, # 동사
"VA": {POS: ADJ}, # 형용사
"VX": {POS: AUX}, # 보조 용언
"VCP": {POS: ADP}, # 긍정 지정사(이다)
"VCN": {POS: ADJ}, # 부정 지정사(아니다)
"NNG": {POS: NOUN}, # 일반 명사(general noun)
"NNB": {POS: NOUN}, # 의존 명사
"NNBC": {POS: NOUN}, # 의존 명사(단위: unit)
"NNP": {POS: PROPN}, # 고유 명사(proper noun)
"NP": {POS: PRON}, # 대명사
"NR": {POS: NUM}, # 수사(numerals)
"SN": {POS: NUM}, # 숫자
# S.{1,2} 부호
# 문장 부호
"SF": {POS: PUNCT}, # period or other EOS marker
"SE": {POS: PUNCT},
"SC": {POS: PUNCT}, # comma, etc.
"SSO": {POS: PUNCT}, # open bracket
"SSC": {POS: PUNCT}, # close bracket
"SY": {POS: SYM}, # 기타 기호
"SL": {POS: X}, # 외국어
"SH": {POS: X}, # 한자
}
| 1,751 | 23 | 104 | py |
spaCy | spaCy-master/spacy/lang/ky/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class KyrgyzDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZER_INFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Kyrgyz(Language):
lang = "ky"
Defaults = KyrgyzDefaults
__all__ = ["Kyrgyz"]
| 487 | 22.238095 | 54 | py |
spaCy | spaCy-master/spacy/lang/ky/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ky.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Apple Улуу Британия стартабын $1 миллиардга сатып алууну көздөөдө.",
"Автоном автомобилдерди камсыздоо жоопкерчилиги өндүрүүчүлөргө артылды.",
"Сан-Франциско тротуар менен жүрүүчү робот-курьерлерге тыю салууну караштырууда.",
"Лондон - Улуу Британияда жайгашкан ири шаар.",
"Кайдасың?",
"Франциянын президенти ким?",
"Америка Кошмо Штаттарынын борбор калаасы кайсы шаар?",
"Барак Обама качан төрөлгөн?",
]
| 599 | 34.294118 | 86 | py |
spaCy | spaCy-master/spacy/lang/ky/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"нөл",
"ноль",
"бир",
"эки",
"үч",
"төрт",
"беш",
"алты",
"жети",
"сегиз",
"тогуз",
"он",
"жыйырма",
"отуз",
"кырк",
"элүү",
"алтымыш",
"жетмиш",
"сексен",
"токсон",
"жүз",
"миң",
"миллион",
"миллиард",
"триллион",
"триллиард",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 800 | 15.346939 | 49 | py |
spaCy | spaCy-master/spacy/lang/ky/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_hyphens_no_dash = HYPHENS.replace("-", "").strip("|").replace("||", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?/()]+(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}{q}])[:<>=](?=[{a}])".format(a=ALPHA, q=CONCAT_QUOTES),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])([{q}\)\]\(\[])(?=[\-{a}])".format(a=ALPHA, q=CONCAT_QUOTES),
r"(?<=[{a}])(?:{h})(?=[{a}])".format(a=ALPHA, h=_hyphens_no_dash),
r"(?<=[0-9])-(?=[{a}])".format(a=ALPHA),
r"(?<=[0-9])-(?=[0-9])",
]
)
TOKENIZER_INFIXES = _infixes
| 855 | 28.517241 | 81 | py |
spaCy | spaCy-master/spacy/lang/ky/stop_words.py | STOP_WORDS = set(
"""
ага адам айтты айтымында айтып ал алар
алардын алган алуу алып анда андан аны
анын ар
бар басма баш башка башкы башчысы берген
биз билдирген билдирди бир биринчи бирок
бишкек болгон болот болсо болуп боюнча
буга бул
гана
да дагы деген деди деп
жана жатат жаткан жаңы же жогорку жок жол
жолу
кабыл калган кандай карата каршы катары
келген керек кийин кол кылмыш кыргыз
күнү көп
маалымат мамлекеттик мен менен миң
мурдагы мыйзам мындай мүмкүн
ошол ошондой
сүрөт сөз
тарабынан турган тууралуу
укук учурда
чейин чек
экенин эки эл эле эмес эми эч
үч үчүн
өз
""".split()
)
| 607 | 13.139535 | 41 | py |
spaCy | spaCy-master/spacy/lang/ky/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
_exc = {}
_abbrev_exc = [
# Weekdays abbreviations
{ORTH: "дүй", NORM: "дүйшөмбү"},
{ORTH: "шей", NORM: "шейшемби"},
{ORTH: "шар", NORM: "шаршемби"},
{ORTH: "бей", NORM: "бейшемби"},
{ORTH: "жум", NORM: "жума"},
{ORTH: "ишм", NORM: "ишемби"},
{ORTH: "жек", NORM: "жекшемби"},
# Months abbreviations
{ORTH: "янв", NORM: "январь"},
{ORTH: "фев", NORM: "февраль"},
{ORTH: "мар", NORM: "март"},
{ORTH: "апр", NORM: "апрель"},
{ORTH: "июн", NORM: "июнь"},
{ORTH: "июл", NORM: "июль"},
{ORTH: "авг", NORM: "август"},
{ORTH: "сен", NORM: "сентябрь"},
{ORTH: "окт", NORM: "октябрь"},
{ORTH: "ноя", NORM: "ноябрь"},
{ORTH: "дек", NORM: "декабрь"},
# Number abbreviations
{ORTH: "млрд", NORM: "миллиард"},
{ORTH: "млн", NORM: "миллион"},
]
for abbr in _abbrev_exc:
for orth in (abbr[ORTH], abbr[ORTH].capitalize(), abbr[ORTH].upper()):
_exc[orth] = [{ORTH: orth, NORM: abbr[NORM]}]
_exc[orth + "."] = [{ORTH: orth + ".", NORM: abbr[NORM]}]
for exc_data in [ # "etc." abbreviations
{ORTH: "ж.б.у.с.", NORM: "жана башка ушул сыяктуу"},
{ORTH: "ж.б.", NORM: "жана башка"},
{ORTH: "ж.", NORM: "жыл"},
{ORTH: "б.з.ч.", NORM: "биздин заманга чейин"},
{ORTH: "б.з.", NORM: "биздин заман"},
{ORTH: "кк.", NORM: "кылымдар"},
{ORTH: "жж.", NORM: "жылдар"},
{ORTH: "к.", NORM: "кылым"},
{ORTH: "көч.", NORM: "көчөсү"},
{ORTH: "м-н", NORM: "менен"},
{ORTH: "б-ча", NORM: "боюнча"},
]:
_exc[exc_data[ORTH]] = [exc_data]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 1,736 | 31.166667 | 74 | py |
spaCy | spaCy-master/spacy/lang/la/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LatinDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
syntax_iterators = SYNTAX_ITERATORS
class Latin(Language):
lang = "la"
Defaults = LatinDefaults
__all__ = ["Latin"]
| 495 | 22.619048 | 54 | py |
spaCy | spaCy-master/spacy/lang/la/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.la.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# > Caes. BG 1.1
# > Cic. De Amic. 1
# > V. Georg. 1.1-5
# > Gen. 1:1
# > Galileo, Sid. Nunc.
# > van Schurman, Opusc. arg. 1
sentences = [
"Gallia est omnis divisa in partes tres, quarum unam incolunt Belgae, aliam Aquitani, tertiam qui ipsorum lingua Celtae, nostra Galli appellantur.",
"Q. Mucius augur multa narrare de C. Laelio socero suo memoriter et iucunde solebat nec dubitare illum in omni sermone appellare sapientem.",
"Quid faciat laetas segetes, quo sidere terram uertere, Maecenas, ulmisque adiungere uitis conueniat, quae cura boum, qui cultus habendo sit pecori, apibus quanta experientia parcis, hinc canere incipiam",
"In principio creavit Deus caelum et terram.",
"Quo sumpto, intelligatur lunaris globus, cuius maximus circulus CAF, centrum vero E, dimetiens CF, qui ad Terre diametrum est ut duo ad septem.",
"Cuicunque natura indita sunt principia, seu potentiae principiorum omnium artium, ac scientiarum, ei conveniunt omnes artes ac scientiae.",
]
| 1,146 | 48.869565 | 209 | py |
spaCy | spaCy-master/spacy/lang/la/lex_attrs.py | import re
from ...attrs import LIKE_NUM
# cf. Goyvaerts/Levithan 2009; case-insensitive, allow 4
roman_numerals_compile = re.compile(
r"(?i)^(?=[MDCLXVI])M*(C[MD]|D?C{0,4})(X[CL]|L?X{0,4})(I[XV]|V?I{0,4})$"
)
_num_words = """unus una unum duo duae tres tria quattuor quinque sex septem octo novem decem undecim duodecim tredecim quattuordecim quindecim sedecim septendecim duodeviginti undeviginti viginti triginta quadraginta quinquaginta sexaginta septuaginta octoginta nonaginta centum ducenti ducentae ducenta trecenti trecentae trecenta quadringenti quadringentae quadringenta quingenti quingentae quingenta sescenti sescentae sescenta septingenti septingentae septingenta octingenti octingentae octingenta nongenti nongentae nongenta mille
""".split()
_num_words += [item.replace("v", "u") for item in _num_words]
_num_words = set(_num_words)
_ordinal_words = """primus prima primum secundus secunda secundum tertius tertia tertium quartus quarta quartum quintus quinta quintum sextus sexta sextum septimus septima septimum octavus octava octavum nonus nona nonum decimus decima decimum undecimus undecima undecimum duodecimus duodecima duodecimum duodevicesimus duodevicesima duodevicesimum undevicesimus undevicesima undevicesimum vicesimus vicesima vicesimum tricesimus tricesima tricesimum quadragesimus quadragesima quadragesimum quinquagesimus quinquagesima quinquagesimum sexagesimus sexagesima sexagesimum septuagesimus septuagesima septuagesimum octogesimus octogesima octogesimum nonagesimus nonagesima nonagesimum centesimus centesima centesimum ducentesimus ducentesima ducentesimum trecentesimus trecentesima trecentesimum quadringentesimus quadringentesima quadringentesimum quingentesimus quingentesima quingentesimum sescentesimus sescentesima sescentesimum septingentesimus septingentesima septingentesimum octingentesimus octingentesima octingentesimum nongentesimus nongentesima nongentesimum millesimus millesima millesimum""".split()
_ordinal_words += [item.replace("v", "u") for item in _ordinal_words]
_ordinal_words = set(_ordinal_words)
def like_num(text):
if text.isdigit():
return True
if roman_numerals_compile.match(text):
return True
if text.lower() in _num_words:
return True
if text.lower() in _ordinal_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 2,372 | 66.8 | 1,111 | py |
spaCy | spaCy-master/spacy/lang/la/stop_words.py | # Corrected Perseus list, cf. https://wiki.digitalclassicist.org/Stopwords_for_Greek_and_Latin
STOP_WORDS = set(
"""
ab ac ad adhuc aliqui aliquis an ante apud at atque aut autem
cum cur
de deinde dum
ego enim ergo es est et etiam etsi ex
fio
haud hic
iam idem igitur ille in infra inter interim ipse is ita
magis modo mox
nam ne nec necque neque nisi non nos
o ob
per possum post pro
quae quam quare qui quia quicumque quidem quilibet quis quisnam quisquam quisque quisquis quo quoniam
sed si sic sive sub sui sum super suus
tam tamen trans tu tum
ubi uel uero
vel vero
""".split()
)
| 619 | 15.315789 | 102 | py |
spaCy | spaCy-master/spacy/lang/la/syntax_iterators.py | from typing import Iterator, Tuple, Union
from ...errors import Errors
from ...symbols import AUX, NOUN, PRON, PROPN, VERB
from ...tokens import Doc, Span
# NB: Modified from da on suggestion from https://github.com/explosion/spaCy/issues/7457#issuecomment-800349751 [PJB]
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
def is_verb_token(tok):
return tok.pos in [VERB, AUX]
def get_left_bound(root):
left_bound = root
for tok in reversed(list(root.lefts)):
if tok.dep in np_left_deps:
left_bound = tok
return left_bound
def get_right_bound(doc, root):
right_bound = root
for tok in root.rights:
if tok.dep in np_right_deps:
right = get_right_bound(doc, tok)
if list(
filter(
lambda t: is_verb_token(t) or t.dep in stop_deps,
doc[root.i : right.i],
)
):
break
else:
right_bound = right
return right_bound
def get_bounds(doc, root):
return get_left_bound(root), get_right_bound(doc, root)
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
if not len(doc):
return
left_labels = [
"det",
"fixed",
"nmod:poss",
"amod",
"flat",
"goeswith",
"nummod",
"appos",
]
right_labels = [
"fixed",
"nmod:poss",
"amod",
"flat",
"goeswith",
"nummod",
"appos",
"nmod",
"det",
]
stop_labels = ["punct"]
np_label = doc.vocab.strings.add("NP")
np_left_deps = [doc.vocab.strings.add(label) for label in left_labels]
np_right_deps = [doc.vocab.strings.add(label) for label in right_labels]
stop_deps = [doc.vocab.strings.add(label) for label in stop_labels]
prev_right = -1
for token in doclike:
if token.pos in [PROPN, NOUN, PRON]:
left, right = get_bounds(doc, token)
if left.i <= prev_right:
continue
yield left.i, right.i + 1, np_label
prev_right = right.i
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
| 2,392 | 26.505747 | 117 | py |
spaCy | spaCy-master/spacy/lang/la/tokenizer_exceptions.py | from ...symbols import ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
## TODO: Look into systematically handling u/v
_exc = {
"mecum": [{ORTH: "me"}, {ORTH: "cum"}],
"tecum": [{ORTH: "te"}, {ORTH: "cum"}],
"nobiscum": [{ORTH: "nobis"}, {ORTH: "cum"}],
"vobiscum": [{ORTH: "vobis"}, {ORTH: "cum"}],
"uobiscum": [{ORTH: "uobis"}, {ORTH: "cum"}],
}
_abbrev_exc = """A. A.D. Aa. Aaa. Acc. Agr. Ap. Apr. April. A.U.C. Aug. C. Caes. Caess. Cc. Cn. Coll. Cons. Conss. Cos. Coss. D. D.N. Dat. Dd. Dec. Decemb. Decembr. F. Feb. Febr. Februar. Ian. Id. Imp. Impp. Imppp. Iul. Iun. K. Kal. L. M'. M. Mai. Mam. Mar. Mart. Med. N. Nn. Nob. Non. Nov. Novemb. Oct. Octob. Opet. Ord. P. Paul. Pf. Pl. Plur. Post. Pp. Prid. Pro. Procos. Q. Quint. S. S.C. Scr. Sept. Septemb. Ser. Sert. Sex. Sext. St. Sta. Suff. T. Ti. Trib. V. Vol. Vop. Vv.""".split()
_abbrev_exc += [item.lower() for item in _abbrev_exc]
_abbrev_exc += [item.upper() for item in _abbrev_exc]
_abbrev_exc += [item.replace("v", "u").replace("V", "U") for item in _abbrev_exc]
_abbrev_exc += ["d.N."]
for orth in set(_abbrev_exc):
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 1,235 | 46.538462 | 489 | py |
spaCy | spaCy-master/spacy/lang/lb/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LuxembourgishDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZER_INFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Luxembourgish(Language):
lang = "lb"
Defaults = LuxembourgishDefaults
__all__ = ["Luxembourgish"]
| 515 | 23.571429 | 54 | py |
spaCy | spaCy-master/spacy/lang/lb/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"An der Zäit hunn sech den Nordwand an d’Sonn gestridden, wie vun hinnen zwee wuel méi staark wier, wéi e Wanderer, deen an ee waarme Mantel agepak war, iwwert de Wee koum.",
"Si goufen sech eens, dass deejéinege fir de Stäerkste gëlle sollt, deen de Wanderer forcéiere géif, säi Mantel auszedoen.",
"Den Nordwand huet mat aller Force geblosen, awer wat e méi geblosen huet, wat de Wanderer sech méi a säi Mantel agewéckelt huet.",
"Um Enn huet den Nordwand säi Kampf opginn.",
"Dunn huet d’Sonn d’Loft mat hire frëndleche Strale gewiermt, a schonn no kuerzer Zäit huet de Wanderer säi Mantel ausgedoen.",
"Do huet den Nordwand missen zouginn, dass d’Sonn vun hinnen zwee de Stäerkste wier.",
]
| 880 | 54.0625 | 178 | py |
spaCy | spaCy-master/spacy/lang/lb/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = set(
"""
null eent zwee dräi véier fënnef sechs ziwen aacht néng zéng eelef zwielef dräizéng
véierzéng foffzéng siechzéng siwwenzéng uechtzeng uechzeng nonnzéng nongzéng zwanzeg drësseg véierzeg foffzeg sechzeg siechzeg siwenzeg achtzeg achzeg uechtzeg uechzeg nonnzeg
honnert dausend millioun milliard billioun billiard trillioun triliard
""".split()
)
_ordinal_words = set(
"""
éischten zweeten drëtten véierten fënneften sechsten siwenten aachten néngten zéngten eeleften
zwieleften dräizéngten véierzéngten foffzéngten siechzéngten uechtzéngen uechzéngten nonnzéngten nongzéngten zwanzegsten
drëssegsten véierzegsten foffzegsten siechzegsten siwenzegsten uechzegsten nonnzegsten
honnertsten dausendsten milliounsten
milliardsten billiounsten billiardsten trilliounsten trilliardsten
""".split()
)
def like_num(text):
"""
check if text resembles a number
"""
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
if text in _ordinal_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 1,308 | 30.926829 | 175 | py |
spaCy | spaCy-master/spacy/lang/lb/punctuation.py | from ..char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER, LIST_ELLIPSES, LIST_ICONS
ELISION = " ' ’ ".strip().replace(" ", "")
abbrev = ("d", "D")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=^[{ab}][{el}])(?=[{a}])".format(ab=abbrev, a=ALPHA, el=ELISION),
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[:<>=](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[0-9])-(?=[0-9])",
]
)
TOKENIZER_INFIXES = _infixes
| 639 | 28.090909 | 85 | py |
spaCy | spaCy-master/spacy/lang/lb/stop_words.py | STOP_WORDS = set(
"""
a
à
äis
är
ärt
äert
ären
all
allem
alles
alleguer
als
also
am
an
anerefalls
ass
aus
awer
bei
beim
bis
bis
d'
dach
datt
däin
där
dat
de
dee
den
deel
deem
deen
deene
déi
den
deng
denger
dem
der
dësem
di
dir
do
da
dann
domat
dozou
drop
du
duerch
duerno
e
ee
em
een
eent
ë
en
ënner
ëm
ech
eis
eise
eisen
eiser
eises
eisereen
esou
een
eng
enger
engem
entweder
et
eréischt
falls
fir
géint
géif
gëtt
gët
geet
gi
ginn
gouf
gouff
goung
hat
haten
hatt
hätt
hei
hu
huet
hun
hunn
hiren
hien
hin
hier
hir
jidderen
jiddereen
jiddwereen
jiddereng
jiddwerengen
jo
ins
iech
iwwer
kann
kee
keen
kënne
kënnt
kéng
kéngen
kéngem
koum
kuckt
mam
mat
ma
mä
mech
méi
mécht
meng
menger
mer
mir
muss
nach
nämmlech
nämmelech
näischt
nawell
nëmme
nëmmen
net
nees
nee
no
nu
nom
och
oder
ons
onsen
onser
onsereen
onst
om
op
ouni
säi
säin
schonn
schonns
si
sid
sie
se
sech
seng
senge
sengem
senger
selwecht
selwer
sinn
sollten
souguer
sou
soss
sot
't
tëscht
u
un
um
virdrun
vu
vum
vun
wann
war
waren
was
wat
wëllt
weider
wéi
wéini
wéinst
wi
wollt
wou
wouhin
zanter
ze
zu
zum
zwar
""".split()
)
| 1,088 | 4.136792 | 17 | py |
spaCy | spaCy-master/spacy/lang/lb/tokenizer_exceptions.py | from ...symbols import NORM, ORTH
from ...util import update_exc
from ..tokenizer_exceptions import BASE_EXCEPTIONS
# TODO
# treat other apostrophes within words as part of the word: [op d'mannst], [fir d'éischt] (= exceptions)
_exc = {}
# translate / delete what is not necessary
for exc_data in [
{ORTH: "’t", NORM: "et"},
{ORTH: "’T", NORM: "et"},
{ORTH: "'t", NORM: "et"},
{ORTH: "'T", NORM: "et"},
{ORTH: "wgl.", NORM: "wannechgelift"},
{ORTH: "M.", NORM: "Monsieur"},
{ORTH: "Mme.", NORM: "Madame"},
{ORTH: "Dr.", NORM: "Dokter"},
{ORTH: "Tel.", NORM: "Telefon"},
{ORTH: "asw.", NORM: "an sou weider"},
{ORTH: "etc.", NORM: "et cetera"},
{ORTH: "bzw.", NORM: "bezéiungsweis"},
{ORTH: "Jan.", NORM: "Januar"},
]:
_exc[exc_data[ORTH]] = [exc_data]
# to be extended
for orth in [
"z.B.",
"Dipl.",
"Dr.",
"etc.",
"i.e.",
"o.k.",
"O.K.",
"p.a.",
"p.s.",
"P.S.",
"phil.",
"q.e.d.",
"R.I.P.",
"rer.",
"sen.",
"ë.a.",
"U.S.",
"U.S.A.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
| 1,160 | 20.90566 | 104 | py |
spaCy | spaCy-master/spacy/lang/lg/__init__.py | from ...language import BaseDefaults, Language
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
class LugandaDefaults(BaseDefaults):
lex_attr_getters = LEX_ATTRS
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
class Luganda(Language):
lang = "lg"
Defaults = LugandaDefaults
__all__ = ["Luganda"]
| 388 | 19.473684 | 46 | py |
spaCy | spaCy-master/spacy/lang/lg/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lg.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Mpa ebyafaayo ku byalo Nakatu ne Nkajja",
"Okuyita Ttembo kitegeeza kugwa ddalu",
"Ekifumu kino kyali kya mulimu ki?",
"Ekkovu we liyise wayitibwa mukululo",
"Akola mulimu ki oguvaamu ssente?",
"Emisumaali egikomerera embaawo giyitibwa nninga",
"Abooluganda ab’emmamba ababiri",
"Ekisaawe ky'ebyenjigiriza kya mugaso nnyo",
]
| 520 | 27.944444 | 56 | py |
spaCy | spaCy-master/spacy/lang/lg/lex_attrs.py | from ...attrs import LIKE_NUM
_num_words = [
"nnooti", # Zero
"zeero", # zero
"emu", # one
"bbiri", # two
"ssatu", # three
"nnya", # four
"ttaano", # five
"mukaaga", # six
"musanvu", # seven
"munaana", # eight
"mwenda", # nine
"kkumi", # ten
"kkumi n'emu", # eleven
"kkumi na bbiri", # twelve
"kkumi na ssatu", # thirteen
"kkumi na nnya", # forteen
"kkumi na ttaano", # fifteen
"kkumi na mukaaga", # sixteen
"kkumi na musanvu", # seventeen
"kkumi na munaana", # eighteen
"kkumi na mwenda", # nineteen
"amakumi abiri", # twenty
"amakumi asatu", # thirty
"amakumi ana", # forty
"amakumi ataano", # fifty
"nkaaga", # sixty
"nsanvu", # seventy
"kinaana", # eighty
"kyenda", # ninety
"kikumi", # hundred
"lukumi", # thousand
"kakadde", # million
"kawumbi", # billion
"kase", # trillion
"katabalika", # quadrillion
"keesedde", # gajillion
"kafukunya", # bazillion
"ekisooka", # first
"ekyokubiri", # second
"ekyokusatu", # third
"ekyokuna", # fourth
"ekyokutaano", # fifith
"ekyomukaaga", # sixth
"ekyomusanvu", # seventh
"eky'omunaana", # eighth
"ekyomwenda", # nineth
"ekyekkumi", # tenth
"ekyekkumi n'ekimu", # eleventh
"ekyekkumi n'ebibiri", # twelveth
"ekyekkumi n'ebisatu", # thirteenth
"ekyekkumi n'ebina", # fourteenth
"ekyekkumi n'ebitaano", # fifteenth
"ekyekkumi n'omukaaga", # sixteenth
"ekyekkumi n'omusanvu", # seventeenth
"ekyekkumi n'omunaana", # eigteenth
"ekyekkumi n'omwenda", # nineteenth
"ekyamakumi abiri", # twentieth
"ekyamakumi asatu", # thirtieth
"ekyamakumi ana", # fortieth
"ekyamakumi ataano", # fiftieth
"ekyenkaaga", # sixtieth
"ekyensanvu", # seventieth
"ekyekinaana", # eightieth
"ekyekyenda", # ninetieth
"ekyekikumi", # hundredth
"ekyolukumi", # thousandth
"ekyakakadde", # millionth
"ekyakawumbi", # billionth
"ekyakase", # trillionth
"ekyakatabalika", # quadrillionth
"ekyakeesedde", # gajillionth
"ekyakafukunya", # bazillionth
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
text_lower = text.lower()
if text_lower in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}
| 2,679 | 26.916667 | 49 | py |
spaCy | spaCy-master/spacy/lang/lg/punctuation.py | from ..char_classes import (
ALPHA,
ALPHA_LOWER,
ALPHA_UPPER,
CONCAT_QUOTES,
HYPHENS,
LIST_ELLIPSES,
LIST_ICONS,
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
]
)
TOKENIZER_INFIXES = _infixes
| 576 | 20.37037 | 68 | py |
spaCy | spaCy-master/spacy/lang/lg/stop_words.py | STOP_WORDS = set(
"""
abadde abalala abamu abangi abava ajja ali alina ani anti ateekeddwa atewamu
atya awamu aweebwa ayinza ba baali babadde babalina bajja
bajjanewankubade bali balina bandi bangi bano bateekeddwa baweebwa bayina bebombi beera bibye
bimu bingi bino bo bokka bonna buli bulijjo bulungi bwabwe bwaffe bwayo bwe bwonna bya byabwe
byaffe byebimu byonna ddaa ddala ddi e ebimu ebiri ebweruobulungi ebyo edda ejja ekirala ekyo
endala engeri ennyo era erimu erina ffe ffenna ga gujja gumu gunno guno gwa gwe kaseera kati
kennyini ki kiki kikino kikye kikyo kino kirungi kki ku kubangabyombi kubangaolwokuba kudda
kuva kuwa kwegamba kyaffe kye kyekimuoyo kyekyo kyonna leero liryo lwa lwaki lyabwezaabwe
lyaffe lyange mbadde mingi mpozzi mu mulinaoyina munda mwegyabwe nolwekyo nabadde nabo nandiyagadde
nandiye nanti naye ne nedda neera nga nnyingi nnyini nnyinza nnyo nti nyinza nze oba ojja okudda
okugenda okuggyako okutuusa okuva okuwa oli olina oluvannyuma olwekyobuva omuli ono osobola otya
oyina oyo seetaaga si sinakindi singa talina tayina tebaali tebaalina tebayina terina tetulina
tetuteekeddwa tewali teyalina teyayina tolina tu tuyina tulina tuyina twafuna twetaaga wa wabula
wabweru wadde waggulunnina wakati waliwobangi waliyo wandi wange wano wansi weebwa yabadde yaffe
ye yenna yennyini yina yonna ziba zijja zonna
""".split()
)
| 1,361 | 67.1 | 99 | py |
spaCy | spaCy-master/spacy/lang/lij/__init__.py | from ...language import BaseDefaults, Language
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
class LigurianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
class Ligurian(Language):
lang = "lij"
Defaults = LigurianDefaults
__all__ = ["Ligurian"]
| 430 | 21.684211 | 54 | py |
spaCy | spaCy-master/spacy/lang/lij/examples.py | """
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lij.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Sciusciâ e sciorbî no se peu.",
"Graçie di çetroin, che me son arrivæ.",
"Vegnime apreuvo, che ve fasso pescâ di òmmi.",
"Bella pe sempre l'ægua inta conchetta quande unn'agoggia d'ægua a se â trapaña.",
]
| 386 | 24.8 | 86 | py |
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