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"""
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Source: DPR Implementation from Facebook Research
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https://github.com/facebookresearch/DPR/tree/master/dpr
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"""
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import string
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import spacy
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import regex
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import unicodedata
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class Tokens(object):
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"""A class to represent a list of tokenized text."""
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TEXT = 0
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TEXT_WS = 1
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SPAN = 2
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POS = 3
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LEMMA = 4
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NER = 5
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def __init__(self, data, annotators, opts=None):
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self.data = data
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self.annotators = annotators
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self.opts = opts or {}
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def __len__(self):
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"""The number of tokens."""
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return len(self.data)
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def slice(self, i=None, j=None):
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"""Return a view of the list of tokens from [i, j)."""
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new_tokens = copy.copy(self)
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new_tokens.data = self.data[i: j]
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return new_tokens
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def untokenize(self):
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"""Returns the original text (with whitespace reinserted)."""
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return ''.join([t[self.TEXT_WS] for t in self.data]).strip()
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def words(self, uncased=False):
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"""Returns a list of the text of each token
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Args:
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uncased: lower cases text
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"""
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if uncased:
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return [t[self.TEXT].lower() for t in self.data]
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else:
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return [t[self.TEXT] for t in self.data]
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def offsets(self):
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"""Returns a list of [start, end) character offsets of each token."""
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return [t[self.SPAN] for t in self.data]
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def pos(self):
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"""Returns a list of part-of-speech tags of each token.
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Returns None if this annotation was not included.
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"""
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if 'pos' not in self.annotators:
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return None
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return [t[self.POS] for t in self.data]
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def lemmas(self):
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"""Returns a list of the lemmatized text of each token.
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Returns None if this annotation was not included.
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"""
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if 'lemma' not in self.annotators:
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return None
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return [t[self.LEMMA] for t in self.data]
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def entities(self):
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"""Returns a list of named-entity-recognition tags of each token.
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Returns None if this annotation was not included.
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"""
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if 'ner' not in self.annotators:
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return None
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return [t[self.NER] for t in self.data]
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def ngrams(self, n=1, uncased=False, filter_fn=None, as_strings=True):
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"""Returns a list of all ngrams from length 1 to n.
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Args:
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n: upper limit of ngram length
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uncased: lower cases text
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filter_fn: user function that takes in an ngram list and returns
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True or False to keep or not keep the ngram
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as_string: return the ngram as a string vs list
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"""
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def _skip(gram):
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if not filter_fn:
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return False
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return filter_fn(gram)
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words = self.words(uncased)
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ngrams = [(s, e + 1)
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for s in range(len(words))
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for e in range(s, min(s + n, len(words)))
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if not _skip(words[s:e + 1])]
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if as_strings:
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ngrams = ['{}'.format(' '.join(words[s:e])) for (s, e) in ngrams]
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return ngrams
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def entity_groups(self):
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"""Group consecutive entity tokens with the same NER tag."""
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entities = self.entities()
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if not entities:
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return None
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non_ent = self.opts.get('non_ent', 'O')
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groups = []
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idx = 0
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while idx < len(entities):
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ner_tag = entities[idx]
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if ner_tag != non_ent:
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start = idx
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while (idx < len(entities) and entities[idx] == ner_tag):
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idx += 1
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groups.append((self.slice(start, idx).untokenize(), ner_tag))
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else:
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idx += 1
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return groups
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class Tokenizer(object):
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"""Base tokenizer class.
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Tokenizers implement tokenize, which should return a Tokens class.
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"""
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def tokenize(self, text):
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raise NotImplementedError
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def shutdown(self):
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pass
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def __del__(self):
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self.shutdown()
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class SimpleTokenizer(Tokenizer):
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ALPHA_NUM = r'[\p{L}\p{N}\p{M}]+'
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NON_WS = r'[^\p{Z}\p{C}]'
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def __init__(self, **kwargs):
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"""
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Args:
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annotators: None or empty set (only tokenizes).
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"""
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self._regexp = regex.compile(
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'(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS),
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flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE
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)
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if len(kwargs.get('annotators', {})) > 0:
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logger.warning('%s only tokenizes! Skipping annotators: %s' %
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(type(self).__name__, kwargs.get('annotators')))
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self.annotators = set()
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def tokenize(self, text):
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data = []
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matches = [m for m in self._regexp.finditer(text)]
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for i in range(len(matches)):
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token = matches[i].group()
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span = matches[i].span()
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start_ws = span[0]
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if i + 1 < len(matches):
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end_ws = matches[i + 1].span()[0]
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else:
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end_ws = span[1]
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data.append((
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token,
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text[start_ws: end_ws],
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span,
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))
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return Tokens(data, self.annotators)
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def has_answer(tokenized_answers, text):
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text = DPR_normalize(text)
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for single_answer in tokenized_answers:
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for i in range(0, len(text) - len(single_answer) + 1):
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if single_answer == text[i: i + len(single_answer)]:
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return True
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return False
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def locate_answers(tokenized_answers, text):
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"""
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Returns each occurrence of an answer as (offset, endpos) in terms of *characters*.
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"""
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tokenized_text = DPR_tokenize(text)
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occurrences = []
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text_words, text_word_positions = tokenized_text.words(uncased=True), tokenized_text.offsets()
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answers_words = [ans.words(uncased=True) for ans in tokenized_answers]
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for single_answer in answers_words:
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for i in range(0, len(text_words) - len(single_answer) + 1):
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if single_answer == text_words[i: i + len(single_answer)]:
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(offset, _), (_, endpos) = text_word_positions[i], text_word_positions[i+len(single_answer)-1]
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occurrences.append((offset, endpos))
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return occurrences
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STokenizer = SimpleTokenizer()
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|
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def DPR_tokenize(text):
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return STokenizer.tokenize(unicodedata.normalize('NFD', text))
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def DPR_normalize(text):
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return DPR_tokenize(text).words(uncased=True)
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|
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def strip_accents(text):
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|
"""Strips accents from a piece of text."""
|
|
text = unicodedata.normalize("NFD", text)
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output = []
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|
for char in text:
|
|
cat = unicodedata.category(char)
|
|
if cat == "Mn":
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continue
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output.append(char)
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return "".join(output)
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|
