一个非常高效的提取内容关键词的python代码

概述一个非常高效的提取内容关键词的python代码

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# 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/# 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()

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