如何在Spacy中获取所有名词短语

Question

如何在Spacy中获取所有名词短语

11

我是Spacy的新手，希望能够从句子中提取“所有”名词短语。我想知道如何实现。我有以下代码：

import spacy

nlp = spacy.load("en")

file = open("E:/test.txt", "r")
doc = nlp(file.read())
for np in doc.noun_chunks:
    print(np.text)

但它只返回基础名词短语，即不包含其他NP的短语。也就是说，对于以下短语，我得到以下结果：

短语: We try to explicitly describe the geometry of the edges of the images. 结果: We, the geometry, the edges, the images.

期望的结果是：We, the geometry, the edges, the images, the geometry of the edges of the images, the edges of the images. 如何获取所有名词短语，包括嵌套的短语？

- user1419243

4个回答

3

对于每个名词块，您还可以获取其下方的子树。Spacy提供了两种访问方式：left_edge和right edge属性以及subtree属性，后者返回Token迭代器而不是跨度。将noun_chunks和它们的子树组合起来会导致一些重复，稍后可以删除。以下是一个使用left_edge和right edge属性的示例。

{np.text
  for nc in doc.noun_chunks
  for np in [
    nc, 
    doc[
      nc.root.left_edge.i
      :nc.root.right_edge.i+1]]}                                                                                                                                                                                                                                                                                                                                                                                                                                                 

==>

{'We',
 'the edges',
 'the edges of the images',
 'the geometry',
 'the geometry of the edges of the images',
 'the images'}

- Daniel Mahler

2

from spacy.matcher import Matcher
import spacy
nlp = spacy.load("en_core_web_sm")

doc = nlp('Features of the iphone applications include a beautiful design, smart search, automatic labels and optional voice responses.') ## sample text
matcher = Matcher(nlp.vocab)
pattern = [{"POS": "NOUN", "OP": "*"}] ## getting all nouns
matcher.add("NOUN_PATTERN", [pattern])
print(matcher(doc, as_spans=True))

获取文本中的所有名词。使用匹配器和模式可以很好地获得您想要的组合。如果您想要更好的模型，请更改"en_core_web_sm"为"en_core_web_bm"

- rad15f

2

请尝试以下方法从文本中获取所有名词：

import spacy
nlp = spacy.load("en_core_web_sm")
text = ("We try to explicitly describe the geometry of the edges of the images.")
doc = nlp(text)
print([chunk.text for chunk in doc.noun_chunks])

- Talha

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Adnan S · Accepted Answer

请查看下面的代码并递归地组合名词。代码灵感来自于Spacy文档这里。

import spacy

nlp = spacy.load("en")

doc = nlp("We try to explicitly describe the geometry of the edges of the images.")

for np in doc.noun_chunks: # use np instead of np.text
    print(np)

print()

# code to recursively combine nouns
# 'We' is actually a pronoun but included in your question
# hence the token.pos_ == "PRON" part in the last if statement
# suggest you extract PRON separately like the noun-chunks above

index = 0
nounIndices = []
for token in doc:
    # print(token.text, token.pos_, token.dep_, token.head.text)
    if token.pos_ == 'NOUN':
        nounIndices.append(index)
    index = index + 1


print(nounIndices)
for idxValue in nounIndices:
    doc = nlp("We try to explicitly describe the geometry of the edges of the images.")
    span = doc[doc[idxValue].left_edge.i : doc[idxValue].right_edge.i+1]
    span.merge()

    for token in doc:
        if token.dep_ == 'dobj' or token.dep_ == 'pobj' or token.pos_ == "PRON":
            print(token.text)