一个非常高效的提取内容关键词的python代码

# 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()

转载出处：http://www.open-open.com/code/view/1428844470065/