mirror of
https://github.com/explosion/spaCy.git
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76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from .doc import Doc
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from ..symbols import HEAD, TAG, DEP, ENT_IOB, ENT_TYPE
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def merge_ents(doc):
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"""Helper: merge adjacent entities into single tokens; modifies the doc."""
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for ent in doc.ents:
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ent.merge(tag=ent.root.tag_, lemma=ent.text, ent_type=ent.label_)
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return doc
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def format_POS(token, light, flat):
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"""Helper: form the POS output for a token."""
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subtree = dict([
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("word", token.text),
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("lemma", token.lemma_), # trigger
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("NE", token.ent_type_), # trigger
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("POS_fine", token.tag_),
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("POS_coarse", token.pos_),
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("arc", token.dep_),
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("modifiers", [])
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])
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if light:
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subtree.pop("lemma")
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subtree.pop("NE")
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if flat:
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subtree.pop("arc")
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subtree.pop("modifiers")
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return subtree
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def POS_tree(root, light=False, flat=False):
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"""Helper: generate a POS tree for a root token. The doc must have
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`merge_ents(doc)` ran on it.
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"""
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subtree = format_POS(root, light=light, flat=flat)
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if not flat:
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for c in root.children:
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subtree["modifiers"].append(POS_tree(c))
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return subtree
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def parse_tree(doc, light=False, flat=False):
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"""Make a copy of the doc and construct a syntactic parse tree similar to
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displaCy. Generates the POS tree for all sentences in a doc.
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doc (Doc): The doc for parsing.
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RETURNS (dict): The parse tree.
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EXAMPLE:
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>>> doc = nlp('Bob brought Alice the pizza. Alice ate the pizza.')
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>>> trees = doc.print_tree()
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>>> trees[1]
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{'modifiers': [
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{'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj',
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'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'},
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{'modifiers': [
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{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det',
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'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}],
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'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN',
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'POS_fine': 'NN', 'lemma': 'pizza'},
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{'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct',
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'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}],
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'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB',
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'POS_fine': 'VBD', 'lemma': 'eat'}
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"""
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doc_clone = Doc(doc.vocab, words=[w.text for w in doc])
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doc_clone.from_array([HEAD, TAG, DEP, ENT_IOB, ENT_TYPE],
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doc.to_array([HEAD, TAG, DEP, ENT_IOB, ENT_TYPE]))
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merge_ents(doc_clone) # merge the entities into single tokens first
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return [POS_tree(sent.root, light=light, flat=flat)
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for sent in doc_clone.sents]
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