使用nltk从文本文件中提取所有名词 [英] Extracting all Nouns from a text file using nltk

问题描述

有没有更有效的方法? 我的代码读取一个文本文件并提取所有名词.

Is there a more efficient way of doing this? My code reads a text file and extracts all Nouns.

import nltk

File = open(fileName) #open file
lines = File.read() #read all lines
sentences = nltk.sent_tokenize(lines) #tokenize sentences
nouns = [] #empty to array to hold all nouns

for sentence in sentences:
     for word,pos in nltk.pos_tag(nltk.word_tokenize(str(sentence))):
         if (pos == 'NN' or pos == 'NNP' or pos == 'NNS' or pos == 'NNPS'):
             nouns.append(word)

如何减少此代码的时间复杂度?有没有办法避免使用嵌套的for循环?

How do I reduce the time complexity of this code? Is there a way to avoid using the nested for loops?

谢谢！

推荐答案

如果您愿意使用除NLTK以外的其他选项，请签出

If you are open to options other than NLTK, check out TextBlob. It extracts all nouns and noun phrases easily:

>>> from textblob import TextBlob
>>> txt = """Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the inter
actions between computers and human (natural) languages."""
>>> blob = TextBlob(txt)
>>> print(blob.noun_phrases)
[u'natural language processing', 'nlp', u'computer science', u'artificial intelligence', u'computational linguistics']

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