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python - 理解 Spacy 的名词块解析器

问题描述

我正在查看 Spacy 提取名词块的代码（转载如下），但我不明白评论的部分：

防止生成嵌套块

for i, word in enumerate(doclike):
            if word.pos not in (NOUN, PROPN, PRON):
                continue
            # Prevent nested chunks from being produced
            if word.left_edge.i <= prev_end:
                continue

我知道我们正在尝试避免嵌套块，但是有人可以向我解释一下这些left_edge方法是如何实现的吗？这是如何跟踪的开始/结束索引noun-chunk？

谢谢！

https://github.com/explosion/spaCy/blob/260c29794a1caa70f8b0702c31fcfecad6bfdadc/spacy/lang/en/syntax_iterators.py

# coding: utf8
from __future__ import unicode_literals

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


def noun_chunks(doclike):
    """
    Detect base noun phrases from a dependency parse. Works on both Doc and Span.
    """
    labels = [
        "nsubj",
        "dobj",
        "nsubjpass",
        "pcomp",
        "pobj",
        "dative",
        "appos",
        "attr",
        "ROOT",
    ]
    doc = doclike.doc  # Ensure works on both Doc and Span.

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

    np_deps = [doc.vocab.strings.add(label) for label in labels]
    conj = doc.vocab.strings.add("conj")
    np_label = doc.vocab.strings.add("NP")
    prev_end = -1
    for i, word in enumerate(doclike):
        if word.pos not in (NOUN, PROPN, PRON):
            continue
        # Prevent nested chunks from being produced
        if word.left_edge.i <= prev_end:
            continue
        if word.dep in np_deps:
            prev_end = word.i
            yield word.left_edge.i, word.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:
                prev_end = word.i
                yield word.left_edge.i, word.i + 1, np_label


SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

标签： pythonspacy

解决方案

有效名词块可以是较大名词块的一部分。例子：

>>> list(nlp("We went to the clean grocery store").noun_chunks)
[We, the clean grocery store]
>>> list(nlp("We went to clean grocery store").noun_chunks)
[We, clean grocery store]
>>> list(nlp("We went to grocery store").noun_chunks)
[We, grocery store]

所以你问的代码是防止list(nlp("We went to the clean grocery store").noun_chunks)返回[We, the clean grocery store, clean grocery store, grocery store]

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