python提取內容關鍵詞的方法

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本文例項講述了python提取內容關鍵詞的方法。分享給大家供大家參考。具體分析如下:

一個非常高效的提取內容關鍵詞的python程式碼,這段程式碼只能用於英文文章內容,中文因為要分詞,這段程式碼就無能為力了,不過要加上分詞功能,效果和英文是一樣的。
複製程式碼 程式碼如下:
# 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()

希望本文所述對大家的Python程式設計有所幫助。

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