为什么我在使用 `nltk.ne_chunk` 分块后没有得到 'PERSON' 和 'GPE' 作为标签？

Question

我正在使用 nltk.ne_chunk() 这样的：

sent="Azhar is asking what is weather in Chicago today? "
chunks = nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent)), binary=True)
print(list(chunks))

并得到这样的输出：

[Tree('NE', [('Azhar', 'NNP')]), ('is', 'VBZ'), ('asking', 'VBG'), ('what', 'WP'), ('is', 
'VBZ'), ('weather', 'NN'), ('in', 'IN'), Tree('NE', [('Chicago', 'NNP')]), ('today', 'NN'), 
('?', '.')]

但我期待这样的输出：

[Tree('PERSON', [('Azhar', 'NNP')]), ('is', 'VBZ'), ('asking', 'VBG'), ('what', 'WP'), ('is', 
'VBZ'), ('weather', 'NN'), ('in', 'IN'), Tree('GPE', [('Chicago', 'NNP')]), ('today', 'NN'), 
('?', '.')]

有人能告诉我我做错了什么吗？

Answer 1

安装好Spacy库并下载相关模型（en_core_web_sm）后here，就可以直接提取Named-Entities了！

import spacy
NER = spacy.load("en_core_web_sm")
sent="Azhar is asking what is weather in Chicago today? "
text1= NER(sent)
for word in text1.ents:
    print(word.text,word.label_)

输出：

Azhar PERSON
Chicago GPE
today DATE

更新

nltk.ne_chunk returns 嵌套的 nltk.tree.Tree 对象，因此您必须遍历 Tree 对象才能到达 NE。来自 nltk.chunk 的 tree2conlltags 会做那样的事情！

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk.chunk import tree2conlltags

sentence = "Azhar is asking what is weather in Chicago today?"
print(tree2conlltags(ne_chunk(pos_tag(word_tokenize(sentence)))))

IOB格式输出：

[('Azhar', 'NNP', 'B-GPE'), ('is', 'VBZ', 'O'), ('asking', 'VBG', 'O'), ('what', 'WP', 'O'), ('is', 'VBZ', 'O'), ('weather', 'NN', 'O'), ('in', 'IN', 'O'), ('Chicago', 'NNP', 'B-GPE'), ('today', 'NN', 'O'), ('?', '.', 'O')]

关于此的更多信息 here！

为什么我在使用 `nltk.ne_chunk` 分块后没有得到 'PERSON' 和 'GPE' 作为标签？

Why I am not getting 'PERSON' nad 'GPE' as label after chunking using `nltk.ne_chunk`?

python

nlp

nltk

tagging

chunking