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。