mirror of
https://github.com/explosion/spaCy.git
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55 lines
2.9 KiB
Python
55 lines
2.9 KiB
Python
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from copy import deepcopy
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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(ent.root.tag_, ent.text, 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, flat):
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'''Helper: generate a POS tree for a root token.
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The doc must have 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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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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"""Makes a copy of the doc, then construct a syntactic parse tree, similar to the one used in displaCy. Generates the POS tree for all sentences in a doc
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Args:
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doc: The doc for parsing.
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Returns:
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[parse_trees (Dict)]:
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>>> from spacy.en import English
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>>> nlp = English()
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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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[{'modifiers': [{'modifiers': [], 'NE': 'PERSON', 'word': 'Bob', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Bob'}, {'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'dobj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'}, {'modifiers': [{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det', 'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}], 'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN', 'POS_fine': 'NN', 'lemma': 'pizza'}, {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}], 'NE': '', 'word': 'brought', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'bring'}, {'modifiers': [{'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'}, {'modifiers': [{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det', 'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}], 'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN', 'POS_fine': 'NN', 'lemma': 'pizza'}, {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}], 'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'eat'}]
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"""
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doc_clone = deepcopy(doc)
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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) for sent in doc_clone.sents]
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