一个非常高效的提取内容关键词的python代码，提取关键词python,# coding=UTF

一个非常高效的提取内容关键词的python代码，提取关键词python,# coding=UTF

# coding=UTF-8import nltkfrom 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/# http://www.byrx.net/codes/# 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()