Python's method of extracting content keywords

This article illustrates an example of how python extracts content keywords. Share with you for your reference. Specific analysis is as follows:

A very efficient extraction of content keywords python code, this code can only be used in English article content, Chinese because of word segmentation, this code is useless, but to add word segmentation function, the effect is the same as in English.

# 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()

I hope this article has helped you with your Python programming.