mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
"""Match a large set of multi-word expressions in O(1) time.
|
|
|
|
The idea is to associate each word in the vocabulary with a tag, noting whether
|
|
they begin, end, or are inside at least one pattern. An additional tag is used
|
|
for single-word patterns. Complete patterns are also stored in a hash set.
|
|
|
|
When we process a document, we look up the words in the vocabulary, to associate
|
|
the words with the tags. We then search for tag-sequences that correspond to
|
|
valid candidates. Finally, we look up the candidates in the hash set.
|
|
|
|
For instance, to search for the phrases "Barack Hussein Obama" and "Hilary Clinton", we
|
|
would associate "Barack" and "Hilary" with the B tag, Hussein with the I tag,
|
|
and Obama and Clinton with the L tag.
|
|
|
|
The document "Barack Clinton and Hilary Clinton" would have the tag sequence
|
|
[{B}, {L}, {}, {B}, {L}], so we'd get two matches. However, only the second candidate
|
|
is in the phrase dictionary, so only one is returned as a match.
|
|
|
|
The algorithm is O(n) at run-time for document of length n because we're only ever
|
|
matching over the tag patterns. So no matter how many phrases we're looking for,
|
|
our pattern set stays very small (exact size depends on the maximum length we're
|
|
looking for, as the query language currently has no quantifiers)
|
|
"""
|
|
from __future__ import print_function, unicode_literals, division
|
|
|
|
import plac
|
|
|
|
from preshed.maps import PreshMap
|
|
from spacy.strings import hash_string
|
|
from spacy.en import English
|
|
from spacy.matcher import Matcher
|
|
|
|
from spacy.attrs import FLAG63 as U_ENT
|
|
from spacy.attrs import FLAG62 as L_ENT
|
|
from spacy.attrs import FLAG61 as I_ENT
|
|
from spacy.attrs import FLAG60 as B_ENT
|
|
|
|
|
|
def get_bilou(length):
|
|
if length == 1:
|
|
return [U_ENT]
|
|
else:
|
|
return [B_ENT] + [I_ENT] * (length - 2) + [L_ENT]
|
|
|
|
|
|
def make_matcher(vocab, max_length):
|
|
abstract_patterns = []
|
|
for length in range(1, max_length+1):
|
|
abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
|
|
return Matcher(vocab, {'Candidate': ('CAND', {}, abstract_patterns)})
|
|
|
|
|
|
def get_matches(matcher, pattern_ids, doc):
|
|
matches = []
|
|
for label, start, end in matcher(doc):
|
|
candidate = doc[start : end]
|
|
if pattern_ids[hash_string(candidate.text)] == True:
|
|
start = candidate[0].idx
|
|
end = candidate[-1].idx + len(candidate[-1])
|
|
matches.append((start, end, candidate.root.tag_, candidate.text))
|
|
return matches
|
|
|
|
|
|
def merge_matches(doc, matches):
|
|
for start, end, tag, text in matches:
|
|
doc.merge(start, end, tag, text, 'MWE')
|
|
|
|
|
|
def main():
|
|
nlp = English(parser=False, tagger=False, entity=False)
|
|
|
|
gazetteer = [u'M.I.A.', 'Shiny Happy People', 'James E. Jones']
|
|
example_text = u'The artist M.I.A. did a cover of Shiny Happy People. People is not an entity.'
|
|
pattern_ids = PreshMap()
|
|
max_length = 0
|
|
for pattern_str in gazetteer:
|
|
pattern = nlp.tokenizer(pattern_str)
|
|
bilou_tags = get_bilou(len(pattern))
|
|
for word, tag in zip(pattern, bilou_tags):
|
|
lexeme = nlp.vocab[word.orth]
|
|
lexeme.set_flag(tag, True)
|
|
pattern_ids[hash_string(pattern.text)] = True
|
|
max_length = max(max_length, len(pattern))
|
|
|
|
matcher = make_matcher(nlp.vocab, max_length)
|
|
|
|
doc = nlp(example_text)
|
|
matches = get_matches(matcher, pattern_ids, doc)
|
|
merge_matches(doc, matches)
|
|
for token in doc:
|
|
print(token.text, token.ent_type_)
|
|
|
|
if __name__ == '__main__':
|
|
plac.call(main)
|