本文实例讲述了python提取内容关键词的方法。分享给大家供大家参考。具体分析如下：
一个非常高效的提取内容关键词的python代码，这段代码只能用于英文文章内容，中文因为要分词，这段代码就无能为力了，不过要加上分词功能，效果和英文是一样的。
代码如下:
# 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()
希望本文所述对大家的python程序设计有所帮助。