在NLP任務中，常需要分析單詞的詞性，借助nltk庫的pos_tag方法可以較好地實現(xiàn)。

以下是一個例子：

import nltk
line = 'i love this world which was beloved by all the people here'
tokens = nltk.word_tokenize(line)
# ['i', 'love', 'this', 'world', 'which', 'was', 'beloved', 'by', 
# 'all', 'the', 'people', 'here']
pos_tags = nltk.pos_tag(tokens)
# [('i', 'RB'), ('love', 'VBP'), ('this', 'DT'), ('world', 'NN'), ('which', 'WDT'), 
# ('was', 'VBD'), ('beloved', 'VBN'), ('by', 'IN'), ('all', 'PDT'), ('the', 'DT'),
# ('people', 'NNS'), ('here', 'RB')]
for word,pos in pos_tags:
     if (pos == 'NN' or pos == 'NNP' or pos == 'NNS' or pos == 'NNPS'):
             print word,pos
# world NN
# people NNS

作為nltk的替代，TextBlob庫能夠更進一步進行詞組劃分，例如“computer science”會被當做一個單詞，而非"computer"和"science"

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']

更多例子請參考nltk官方教科書第五章
其中pos_tag分析出來的詞性含義按照賓夕法尼亞大學tag詞性對照表