spaCy/spacy/lang/es/syntax_iterators.py

# coding: utf8
from __future__ import unicode_literals

from ...symbols import NOUN, PROPN, PRON, VERB, AUX


def noun_chunks(obj):
    doc = obj.doc
    if not len(doc):
        return
    np_label = doc.vocab.strings.add('NP')
    left_labels = ['det', 'fixed', 'neg'] #['nunmod', 'det', 'appos', 'fixed']
    right_labels = ['flat', 'fixed', 'compound', 'neg']
    stop_labels = ['punct']
    np_left_deps = [doc.vocab.strings.add(label) for label in left_labels]
    np_right_deps = [doc.vocab.strings.add(label) for label in right_labels]
    stop_deps = [doc.vocab.strings.add(label) for label in stop_labels]
    token = doc[0]
    while token and token.i < len(doc):
        if token.pos in [PROPN, NOUN, PRON]:
            left, right = noun_bounds(doc, token, np_left_deps, np_right_deps, stop_deps)
            yield left.i, right.i+1, np_label
            token = right
        token = next_token(token)


def is_verb_token(token):
    return token.pos in [VERB, AUX]


def next_token(token):
    try:
        return token.nbor()
    except:
        return None


def noun_bounds(doc, root, np_left_deps, np_right_deps, stop_deps):
    left_bound = root
    for token in reversed(list(root.lefts)):
        if token.dep in np_left_deps:
            left_bound = token
    right_bound = root
    for token in root.rights:
        if (token.dep in np_right_deps):
            left, right = noun_bounds(doc, token, np_left_deps, np_right_deps, stop_deps)
            if list(filter(lambda t: is_verb_token(t) or t.dep in stop_deps,
                           doc[left_bound.i: right.i])):
                break
            else:
                right_bound = right
    return left_bound, right_bound


SYNTAX_ITERATORS = {
    'noun_chunks': noun_chunks
}