spaCy/spacy/lang/nb/syntax_iterators.py

# coding: utf8
from __future__ import unicode_literals

from ...symbols import NOUN, PROPN, PRON
from ...errors import Errors


def noun_chunks(obj):
    """
    Detect base noun phrases from a dependency parse. Works on both Doc and Span.
    """
    labels = [
        "nsubj",
        "nsubj:pass",
        "obj",
        "iobj",
        "ROOT",
        "appos",
        "nmod",
        "nmod:poss",
    ]
    doc = obj.doc  # Ensure works on both Doc and Span.

    if not doc.is_parsed:
        raise ValueError(Errors.E029)

    np_deps = [doc.vocab.strings[label] for label in labels]
    conj = doc.vocab.strings.add("conj")
    np_label = doc.vocab.strings.add("NP")
    seen = set()
    for i, word in enumerate(obj):
        if word.pos not in (NOUN, PROPN, PRON):
            continue
        # Prevent nested chunks from being produced
        if word.i in seen:
            continue
        if word.dep in np_deps:
            w_range = range(word.left_edge.i, word.right_edge.i + 1)
            if any(j in seen for j in w_range):
                continue
            seen.update(j for j in w_range)
            yield word.left_edge.i, word.right_edge.i + 1, np_label
        elif word.dep == conj:
            head = word.head
            while head.dep == conj and head.head.i < head.i:
                head = head.head
            # If the head is an NP, and we're coordinated to it, we're an NP
            if head.dep in np_deps:
                w_range = range(word.left_edge.i, word.right_edge.i + 1)
                if any(j in seen for j in w_range):
                    continue
                seen.update(j for j in w_range)
                yield word.left_edge.i, word.right_edge.i + 1, np_label


SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}