mirror of https://github.com/explosion/spaCy.git
* Fix multi word matcher
This commit is contained in:
Matthew Honnibal
parent
801d55a6d9
commit
4bbc8f45c6
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@ -26,137 +26,71 @@ from ast import literal_eval
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from bz2 import BZ2File
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import time
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import math
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import codecs
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import plac
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from preshed.maps import PreshMap
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from preshed.counter import PreshCounter
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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 B_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 B2_ENT
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from spacy.attrs import FLAG59 as B3_ENT
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from spacy.attrs import FLAG58 as B4_ENT
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from spacy.attrs import FLAG57 as B5_ENT
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from spacy.attrs import FLAG56 as B6_ENT
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from spacy.attrs import FLAG55 as B7_ENT
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from spacy.attrs import FLAG54 as B8_ENT
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from spacy.attrs import FLAG53 as B9_ENT
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from spacy.attrs import FLAG52 as B10_ENT
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from spacy.attrs import FLAG51 as I3_ENT
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from spacy.attrs import FLAG50 as I4_ENT
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from spacy.attrs import FLAG49 as I5_ENT
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from spacy.attrs import FLAG48 as I6_ENT
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from spacy.attrs import FLAG47 as I7_ENT
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from spacy.attrs import FLAG46 as I8_ENT
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from spacy.attrs import FLAG45 as I9_ENT
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from spacy.attrs import FLAG44 as I10_ENT
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from spacy.attrs import FLAG43 as L2_ENT
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from spacy.attrs import FLAG42 as L3_ENT
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from spacy.attrs import FLAG41 as L4_ENT
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from spacy.attrs import FLAG40 as L5_ENT
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from spacy.attrs import FLAG39 as L6_ENT
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from spacy.attrs import FLAG38 as L7_ENT
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from spacy.attrs import FLAG37 as L8_ENT
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from spacy.attrs import FLAG36 as L9_ENT
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from spacy.attrs import FLAG35 as L10_ENT
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from spacy.matcher import PhraseMatcher
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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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elif length == 2:
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return [B2_ENT, L2_ENT]
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elif length == 3:
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return [B3_ENT, I3_ENT, L3_ENT]
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elif length == 4:
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return [B4_ENT, I4_ENT, I4_ENT, L4_ENT]
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elif length == 5:
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return [B5_ENT, I5_ENT, I5_ENT, L5_ENT]
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elif length == 6:
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return [B6_ENT, I6_ENT, I6_ENT, I6_ENT, I6_ENT, L6_ENT]
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elif length == 7:
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return [B7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, L7_ENT]
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elif length == 8:
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return [B8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, L8_ENT]
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elif length == 9:
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return [B9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, L9_ENT]
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elif length == 10:
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return [B10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, L10_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(2, max_length):
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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 read_gazetteer(loc):
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for line in open(loc):
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def read_gazetteer(tokenizer, loc, n=-1):
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for i, line in enumerate(open(loc)):
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phrase = literal_eval('u' + line.strip())
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if ' (' in phrase and phrase.endswith(')'):
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phrase = phrase.split(' (', 1)[0]
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if i >= n:
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break
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phrase = tokenizer(phrase)
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if len(phrase) >= 2:
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yield phrase
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def read_text(bz2_loc):
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with BZ2File(bz2_loc) as file_:
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for line in file_:
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yield line.decode('utf8')
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def main(patterns_loc, text_loc):
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def get_matches(tokenizer, phrases, texts, max_length=6):
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matcher = PhraseMatcher(tokenizer.vocab, phrases, max_length=max_length)
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print("Match")
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for text in texts:
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doc = tokenizer(text)
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matches = matcher(doc)
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for mwe in doc.ents:
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yield mwe
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def main(patterns_loc, text_loc, counts_loc, n=10000000):
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nlp = English(parser=False, tagger=False, entity=False)
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pattern_ids = PreshMap()
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max_length = 10
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i = 0
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for pattern_str in read_gazetteer(patterns_loc):
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pattern = nlp.tokenizer(pattern_str)
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if len(pattern) < 2 or len(pattern) >= max_length:
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continue
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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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i += 1
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if i >= 10000001:
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break
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matcher = make_matcher(nlp.vocab, max_length)
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print("Make matcher")
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phrases = read_gazetteer(nlp.tokenizer, patterns_loc, n=n)
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counts = PreshCounter()
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t1 = time.time()
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for text in read_text(text_loc):
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doc = nlp.tokenizer(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 mwe in get_matches(nlp.tokenizer, phrases, read_text(text_loc)):
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counts.inc(hash_string(mwe.text), 1)
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t2 = time.time()
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print('10 ^ %d patterns took %d s' % (round(math.log(i, 10)), t2-t1))
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print("10m tokens in %d s" % (t2 - t1))
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with codecs.open(counts_loc, 'w', 'utf8') as file_:
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for phrase in read_gazetteer(nlp.tokenizer, patterns_loc, n=n):
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text = phrase.string
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key = hash_string(text)
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count = counts[key]
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if count != 0:
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file_.write('%d\t%s\n' % (count, text))
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if __name__ == '__main__':
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if False:
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import cProfile
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import pstats
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cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
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s = pstats.Stats("Profile.prof")
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s.strip_dirs().sort_stats("time").print_stats()
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else:
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plac.call(main)
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