一個非常高效的提取內容關鍵詞的python代碼，這段代碼只能用于英文文章內容，中文因為要分詞，這段代碼就無能為力了，不過要加上分詞功能，效果和英文是一樣的。

# 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()</pre>