mirror of
https://github.com/explosion/spaCy.git
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122 lines
4.2 KiB
Cython
122 lines
4.2 KiB
Cython
# coding: utf-8
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from __future__ import unicode_literals
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from ..parts_of_speech cimport NOUN, PROPN, PRON, VERB, AUX
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def english_noun_chunks(obj):
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"""
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Detect base noun phrases from a dependency parse.
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Works on both Doc and Span.
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"""
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labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj', 'dative', 'appos',
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'attr', 'ROOT']
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doc = obj.doc # Ensure works on both Doc and Span.
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np_deps = [doc.vocab.strings[label] for label in labels]
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conj = doc.vocab.strings['conj']
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np_label = doc.vocab.strings['NP']
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seen = set()
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for i, word in enumerate(obj):
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if word.pos not in (NOUN, PROPN, PRON):
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continue
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# Prevent nested chunks from being produced
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if word.i in seen:
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continue
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if word.dep in np_deps:
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if any(w.i in seen for w in word.subtree):
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continue
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seen.update(j for j in range(word.left_edge.i, word.i+1))
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yield word.left_edge.i, word.i+1, np_label
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elif word.dep == conj:
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head = word.head
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while head.dep == conj and head.head.i < head.i:
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head = head.head
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# If the head is an NP, and we're coordinated to it, we're an NP
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if head.dep in np_deps:
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if any(w.i in seen for w in word.subtree):
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continue
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seen.update(j for j in range(word.left_edge.i, word.i+1))
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yield word.left_edge.i, word.i+1, np_label
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# this iterator extracts spans headed by NOUNs starting from the left-most
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# syntactic dependent until the NOUN itself
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# for close apposition and measurement construction, the span is sometimes
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# extended to the right of the NOUN
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# example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee" and not
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# just "eine Tasse", same for "das Thema Familie"
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def german_noun_chunks(obj):
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labels = ['sb', 'oa', 'da', 'nk', 'mo', 'ag', 'ROOT', 'root', 'cj', 'pd', 'og', 'app']
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doc = obj.doc # Ensure works on both Doc and Span.
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np_label = doc.vocab.strings['NP']
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np_deps = set(doc.vocab.strings[label] for label in labels)
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close_app = doc.vocab.strings['nk']
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rbracket = 0
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for i, word in enumerate(obj):
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if i < rbracket:
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continue
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if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps:
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rbracket = word.i+1
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# try to extend the span to the right
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# to capture close apposition/measurement constructions
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for rdep in doc[word.i].rights:
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if rdep.pos in (NOUN, PROPN) and rdep.dep == close_app:
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rbracket = rdep.i+1
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yield word.left_edge.i, rbracket, np_label
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def es_noun_chunks(obj):
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doc = obj.doc
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np_label = doc.vocab.strings['NP']
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left_labels = ['det', 'fixed', 'neg'] #['nunmod', 'det', 'appos', 'fixed']
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right_labels = ['flat', 'fixed', 'compound', 'neg']
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stop_labels = ['punct']
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np_left_deps = [doc.vocab.strings[label] for label in left_labels]
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np_right_deps = [doc.vocab.strings[label] for label in right_labels]
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stop_deps = [doc.vocab.strings[label] for label in stop_labels]
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def next_token(token):
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try:
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return token.nbor()
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except:
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return None
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def noun_bounds(root):
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def is_verb_token(token):
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return token.pos in [VERB, AUX]
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left_bound = root
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for token in reversed(list(root.lefts)):
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if token.dep in np_left_deps:
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left_bound = token
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right_bound = root
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for token in root.rights:
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if (token.dep in np_right_deps):
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left, right = noun_bounds(token)
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if list(filter(lambda t: is_verb_token(t) or t.dep in stop_deps, doc[left_bound.i: right.i])):
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break
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else:
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right_bound = right
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return left_bound, right_bound
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token = doc[0]
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while token and token.i < len(doc):
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if token.pos in [PROPN, NOUN, PRON]:
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left, right = noun_bounds(token)
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yield left.i, right.i+1, np_label
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token = right
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token = next_token(token)
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CHUNKERS = {'en': english_noun_chunks, 'de': german_noun_chunks, 'es': es_noun_chunks,
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None: english_noun_chunks, '': english_noun_chunks}
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