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add new files npchunks.{pyx,pxd} to hold noun phrase chunk generators
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2
setup.py
2
setup.py
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@ -56,6 +56,7 @@ MOD_NAMES = [
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'spacy.tokens.doc',
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'spacy.tokens.span',
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'spacy.tokens.token',
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'spacy.tokens.npchunks',
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'spacy.serialize.packer',
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'spacy.serialize.huffman',
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'spacy.serialize.bits',
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@ -184,3 +185,4 @@ def setup_package():
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if __name__ == '__main__':
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setup_package()
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@ -23,6 +23,7 @@ from .token cimport Token
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from ..serialize.bits cimport BitArray
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from ..util import normalize_slice
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import npchunks
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DEF PADDING = 5
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@ -240,23 +241,14 @@ cdef class Doc:
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"\npython -m spacy.en.download all\n"
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"to install the data")
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cdef const TokenC* word
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labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
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'attr', 'root']
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np_deps = [self.vocab.strings[label] for label in labels]
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conj = self.vocab.strings['conj']
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np_label = self.vocab.strings['NP']
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for i in range(self.length):
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word = &self.c[i]
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if word.pos == NOUN and word.dep in np_deps:
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yield Span(self, word.l_edge, i+1, label=np_label)
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elif word.pos == NOUN and word.dep == conj:
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head = word+word.head
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while head.dep == conj and head.head < 0:
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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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yield Span(self, word.l_edge, i+1, label=np_label)
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chunk_rules = {'en':npchunks.english, 'de':npchunks.german}
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for sent in self.sents:
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lang = 'en' # todo: make dependent on language of root token
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for chunk in chunk_rules.get(lang)(sent):
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yield chunk
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@property
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def sents(self):
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0
spacy/tokens/npchunks.pxd
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0
spacy/tokens/npchunks.pxd
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54
spacy/tokens/npchunks.pyx
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54
spacy/tokens/npchunks.pyx
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@ -0,0 +1,54 @@
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from ..structs cimport TokenC
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from .doc cimport Doc
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from .span cimport Span
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from ..parts_of_speech cimport NOUN, PROPN, PRON
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def english(Span sent):
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cdef const TokenC* word
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strings = sent.doc.vocab.strings
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labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj', 'attr', 'root']
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np_deps = [strings[label] for label in labels]
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conj = strings['conj']
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np_label = strings['NP']
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for i in range(sent.start, sent.end):
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word = &sent.doc.c[i]
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if word.pos == NOUN and word.dep in np_deps:
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yield Span(sent.doc, word.l_edge, i+1, label=np_label)
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elif word.pos == NOUN and word.dep == conj:
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head = word+word.head
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while head.dep == conj and head.head < 0:
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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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yield Span(sent.doc, word.l_edge, i+1, label=np_label)
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def german(Span sent):
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# this function 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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cdef const TokenC* word
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strings = sent.doc.vocab.strings
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labels = ['sb', 'oa', 'da', 'nk', 'mo', 'ag', 'root', 'cj', 'pd', 'og', 'app']
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close_app = strings['nk']
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np_deps = [strings[label] for label in labels]
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np_label = strings['NP']
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for i in range(sent.start, sent.end):
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word = &sent.doc.c[i]
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if word.pos == NOUN and word.dep in np_deps:
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rbracket = 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 sent.doc[i].rights:
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if rdep.pos == NOUN and rdep.dep == close_app:
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rbracket = rdep.i+1
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yield Span(sent.doc, word.l_edge, rbracket, label=np_label)
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