本文实例讲述了python提取内容关键词的方法。分享给大家供大家参考。具体分析如下：

一个非常高效的提取内容关键词的python代码，这段代码只能用于英文文章内容，中文因为要分词，这段代码就无能为力了，不过要加上分词功能，效果和英文是一样的。

复制代码 代码如下:

coding=UTF-8

import nltk
from nltk.corpus import brown

This is a fast and simple noun phrase extractor (based on NLTK)

Feel free to use it, just keep a link back to this post

http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/

Create by Shlomi Babluki

May, 2013

This is our fast Part of Speech tagger

#############################################################################
brown_train = brown.tagged_sents(categories='news')
regexp_tagger = nltk.RegexpTagger(
[(r'^-?[0-9]+(.[0-9]+)?$', 'CD'),
(r'(-|:|;)$', ':'),
(r'\'$', 'MD'),
(r'(The|the|A|a|An|an)$', 'AT'),
(r'.able$', 'JJ'),
(r'^[A-Z].$', 'NNP'),
(r'.ness$', 'NN'),
(r'.ly$', 'RB'),
(r'.s$', 'NNS'),
(r'.ing$', 'VBG'),
(r'.ed$', 'VBD'),
(r'.*', 'NN')
])
unigram_tagger = nltk.UnigramTagger(brown_train, backoff=regexp_tagger)
bigram_tagger = nltk.BigramTagger(brown_train, backoff=unigram_tagger)
#############################################################################

This is our semi-CFG; Extend it according to your own needs

#############################################################################
cfg = {}
cfg["NNP+NNP"] = "NNP"
cfg["NN+NN"] = "NNI"
cfg["NNI+NN"] = "NNI"
cfg["JJ+JJ"] = "JJ"
cfg["JJ+NN"] = "NNI"
#############################################################################
class NPExtractor(object):
def init(self, sentence):
self.sentence = sentence

Split the sentence into singlw words/tokens

def tokenize_sentence(self, sentence):  
    tokens = nltk.word_tokenize(sentence)  
    return tokens  
# Normalize brown corpus' tags ("NN", "NN-PL", "NNS" > "NN")  
def normalize_tags(self, tagged):  
    n_tagged = []  
    for t in tagged:  
        if t[1] == "NP-TL" or t[1] == "NP":  
            n_tagged.append((t[0], "NNP"))  
            continue  
        if t[1].endswith("-TL"):  
            n_tagged.append((t[0], t[1][:-3]))  
            continue  
        if t[1].endswith("S"):  
            n_tagged.append((t[0], t[1][:-1]))  
            continue  
        n_tagged.append((t[0], t[1]))  
    return n_tagged  
# Extract the main topics from the sentence  
def extract(self):  
    tokens = self.tokenize_sentence(self.sentence)  
    tags = self.normalize_tags(bigram_tagger.tag(tokens))  
    merge = True  
    while merge:  
        merge = False  
        for x in range(0, len(tags) - 1):  
            t1 = tags[x]  
            t2 = tags[x + 1]  
            key = "%s+%s" % (t1[1], t2[1])  
            value = cfg.get(key, '')  
            if value:  
                merge = True  
                tags.pop(x)  
                tags.pop(x)  
                match = "%s %s" % (t1[0], t2[0])  
                pos = value  
                tags.insert(x, (match, pos))  
                break  
    matches = []  
    for t in tags:  
        if t[1] == "NNP" or t[1] == "NNI":  
        #if t[1] == "NNP" or t[1] == "NNI" or t[1] == "NN":  
            matches.append(t[0])  
    return matches  

Main method, just run "python np_extractor.py"

def main():
sentence = "Swayy is a beautiful new dashboard for discovering and curating online content."
np_extractor = NPExtractor(sentence)
result = np_extractor.extract()
print "This sentence is about: %s" % ", ".join(result)
if name == 'main':
main()

希望本文所述对大家的Python程序设计有所帮助。