"""Metaphor corpus KOMET 1.0"""
import os
import re
import xml.etree.ElementTree as ET
from typing import List, Tuple
import datasets
_CITATION = """\
@InProceedings{antloga2020komet,
title = {Korpus metafor KOMET 1.0},
author={Antloga, \v{S}pela},
booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student abstracts)},
year={2020},
pages={167-170}
}
"""
_DESCRIPTION = """\
KOMET 1.0 is a hand-annotated corpus for metaphorical expressions which contains about 200,000 words from
Slovene journalistic, fiction and on-line texts.
To annotate metaphors in the corpus an adapted and modified procedure of the MIPVU protocol
(Steen et al., 2010: A method for linguistic metaphor identification: From MIP to MIPVU, https://www.benjamins.com/catalog/celcr.14)
was used. The lexical units (words) whose contextual meanings are opposed to their basic meanings are considered
metaphor-related words. The basic and contextual meaning for each word in the corpus was identified using the
Dictionary of the standard Slovene Language. The corpus was annotated for the metaphoric following relations:
indirect metaphor (MRWi), direct metaphor (MRWd), borderline case (WIDLI) and metaphor signal (MFlag).
In addition, the corpus introduces a new 'frame' tag, which gives information about the concept to which it refers.
"""
_HOMEPAGE = "http://hdl.handle.net/11356/1293"
_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
_URLS = {
"komet": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1293/komet.tei.zip"
}
XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
EL_LEAF, EL_TYPE, EL_FRAME = range(3)
def namespace(element):
# https://stackoverflow.com/a/12946675
m = re.match(r'\{.*\}', element.tag)
return m.group(0) if m else ''
def word_info(sent_el):
def _resolve_recursively(element) -> List:
""" Knowingly ignored tags: name (anonymized, without IDs), gap, vocal, pause, del,
linkGrp (syntactic dependencies) """
# Leaf node: word or punctuation character
if element.tag.endswith(("w", "pc")):
id_curr = element.attrib[f"{XML_NAMESPACE}id"]
return [(id_curr, element.text)]
# Annotated word or word group - not interested in the annotations in this function
elif element.tag.endswith("seg"):
parsed_data = []
for child in element:
if child.tag.endswith("c") and not child.tag.endswith("pc"): # empty space betw. words
continue
res = _resolve_recursively(child)
if isinstance(res, list):
parsed_data.extend(res)
else:
parsed_data.append(res)
return parsed_data
id_words, words = [], []
for child_el in sent_el:
curr_annotations = _resolve_recursively(child_el)
if curr_annotations is not None: # None = unrecognized ("unimportant") element
for ann in curr_annotations:
id_words.append(ann[0])
words.append(ann[1])
return id_words, words
def seg_info(sent_el):
def _resolve_recursively(element) -> Tuple:
""" Returns (type[, subtype], deeper_elements, latest_element)"""
# Leaf node: word or punctuation character
if element.tag.endswith(("w", "pc")):
id_curr = element.attrib[f"{XML_NAMESPACE}id"]
return EL_LEAF, [], [id_curr]
# Annotated word or word group
elif element.tag.endswith("seg"):
if element.attrib["subtype"] == "frame":
ann_type, subtype = EL_FRAME, element.attrib["ana"]
if subtype.startswith("#met."): # for consistency with G-Komet, remove "#met." prefix from frames
subtype = subtype[5:]
elif element.attrib["type"] == "metaphor":
ann_type = EL_TYPE
subtype = element.attrib["subtype"]
else:
raise ValueError(f"Unrecognized seg type: {element.attrib['type']}")
deeper_elements = []
latest_element = []
for child in element:
if child.tag.endswith("c") and not child.tag.endswith("pc"): # empty space betw. words
continue
res = _resolve_recursively(child)
if res[0] == EL_LEAF:
latest_element.extend(res[2])
else:
deeper_elements.extend(res[2])
deeper_elements.append((res[0], res[1], res[3]))
latest_element.extend(res[3])
return ann_type, subtype, deeper_elements, latest_element
annotations = []
for child_el in sent_el:
if not child_el.tag.endswith("seg"):
continue
ann_type, subtype, deeper_elements, latest_element = _resolve_recursively(child_el)
annotations.extend(deeper_elements)
annotations.append((ann_type, subtype, latest_element))
return annotations
class Komet(datasets.GeneratorBasedBuilder):
"""KOMET is a hand-annotated Slovenian corpus of metaphorical expressions."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"document_name": datasets.Value("string"),
"idx": datasets.Value("uint32"), # index inside current document
"idx_paragraph": datasets.Value("uint32"),
"idx_sentence": datasets.Value("uint32"), # index inside current paragraph
"sentence_words": datasets.Sequence(datasets.Value("string")),
"met_type": [{
"type": datasets.Value("string"),
"word_indices": datasets.Sequence(datasets.Value("uint32"))
}],
"met_frame": [{
"type": datasets.Value("string"),
"word_indices": datasets.Sequence(datasets.Value("uint32"))
}]
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS["komet"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": os.path.join(data_dir, "komet.tei")},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, data_dir):
data_files = []
for fname in os.listdir(data_dir):
curr_path = os.path.join(data_dir, fname)
if os.path.isfile(curr_path) and fname.endswith(".xml") and fname != "komet.xml": # komet.xml = meta-file
data_files.append(fname)
data_files = sorted(data_files)
idx_example = 0
for fname in data_files:
fpath = os.path.join(data_dir, fname)
curr_doc = ET.parse(fpath)
root = curr_doc.getroot()
NAMESPACE = namespace(root)
idx_sent_glob = 0
for idx_par, curr_par in enumerate(root.iterfind(f".//{NAMESPACE}p")):
id2position = {} # {<idx_sent> -> {<id_word>: <position> foreach word} foreach sent}
all_words = []
# Pass#1: extract word information
for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
id_words, words = word_info(curr_sent)
id2position[idx_sent] = dict(zip(id_words, range(len(words))))
all_words.append(words)
all_types, all_frames = [], []
# Pass#2: extract annotations from <seg>ments
for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
annotated_segs = seg_info(curr_sent)
all_types.append([])
all_frames.append([])
for curr_ann in annotated_segs:
ann_type, ann_subtype, words_involved = curr_ann
if ann_type == EL_TYPE:
all_types[idx_sent].append({
"type": ann_subtype,
"word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
if _id_word in id2position[idx_sent]]
})
elif ann_type == EL_FRAME:
all_frames[idx_sent].append({
"type": ann_subtype,
"word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
if _id_word in id2position[idx_sent]]
})
idx_sent = 0
for curr_words, curr_types, curr_frames in zip(all_words, all_types, all_frames):
if len(curr_words) == 0:
continue
yield idx_example, {
"document_name": fname,
"idx": idx_sent_glob,
"idx_paragraph": idx_par,
"idx_sentence": idx_sent,
"sentence_words": curr_words,
"met_type": curr_types,
"met_frame": curr_frames
}
idx_example += 1
idx_sent += 1
idx_sent_glob += 1