mirror of https://github.com/explosion/spaCy.git
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
"""Match a large set of multi-word expressions in O(1) time.
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The idea is to associate each word in the vocabulary with a tag, noting whether
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they begin, end, or are inside at least one pattern. An additional tag is used
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for single-word patterns. Complete patterns are also stored in a hash set.
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When we process a document, we look up the words in the vocabulary, to associate
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the words with the tags. We then search for tag-sequences that correspond to
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valid candidates. Finally, we look up the candidates in the hash set.
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For instance, to search for the phrases "Barack Hussein Obama" and "Hilary Clinton", we
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would associate "Barack" and "Hilary" with the B tag, Hussein with the I tag,
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and Obama and Clinton with the L tag.
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The document "Barack Clinton and Hilary Clinton" would have the tag sequence
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[{B}, {L}, {}, {B}, {L}], so we'd get two matches. However, only the second candidate
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is in the phrase dictionary, so only one is returned as a match.
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The algorithm is O(n) at run-time for document of length n because we're only ever
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matching over the tag patterns. So no matter how many phrases we're looking for,
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our pattern set stays very small (exact size depends on the maximum length we're
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looking for, as the query language currently has no quantifiers)
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"""
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from __future__ import print_function, unicode_literals, division
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import plac
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from preshed.maps import PreshMap
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from spacy.strings import hash_string
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from spacy.en import English
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from spacy.matcher import Matcher
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from spacy.attrs import FLAG63 as U_ENT
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from spacy.attrs import FLAG62 as L_ENT
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from spacy.attrs import FLAG61 as I_ENT
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from spacy.attrs import FLAG60 as B_ENT
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def get_bilou(length):
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if length == 1:
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return [U_ENT]
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else:
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return [B_ENT] + [I_ENT] * (length - 2) + [L_ENT]
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def make_matcher(vocab, max_length):
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abstract_patterns = []
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for length in range(1, max_length+1):
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abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
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return Matcher(vocab, {'Candidate': ('CAND', {}, abstract_patterns)})
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def get_matches(matcher, pattern_ids, doc):
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matches = []
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for label, start, end in matcher(doc):
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candidate = doc[start : end]
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if pattern_ids[hash_string(candidate.text)] == True:
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start = candidate[0].idx
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end = candidate[-1].idx + len(candidate[-1])
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matches.append((start, end, candidate.root.tag_, candidate.text))
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return matches
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def merge_matches(doc, matches):
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for start, end, tag, text in matches:
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doc.merge(start, end, tag, text, 'MWE')
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def main():
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nlp = English(parser=False, tagger=False, entity=False)
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gazetteer = [u'M.I.A.', 'Shiny Happy People', 'James E. Jones']
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example_text = u'The artist M.I.A. did a cover of Shiny Happy People. People is not an entity.'
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pattern_ids = PreshMap()
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max_length = 0
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for pattern_str in gazetteer:
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pattern = nlp.tokenizer(pattern_str)
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bilou_tags = get_bilou(len(pattern))
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for word, tag in zip(pattern, bilou_tags):
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lexeme = nlp.vocab[word.orth]
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lexeme.set_flag(tag, True)
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pattern_ids[hash_string(pattern.text)] = True
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max_length = max(max_length, len(pattern))
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matcher = make_matcher(nlp.vocab, max_length)
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doc = nlp(example_text)
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matches = get_matches(matcher, pattern_ids, doc)
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merge_matches(doc, matches)
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for token in doc:
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print(token.text, token.ent_type_)
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if __name__ == '__main__':
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plac.call(main)
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