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python提取内容关键词的方法

2024-06-01 08:10:48 收藏本文下载本文

“yalin-shi”通过精心收集，向本站投稿了5篇python提取内容关键词的方法，以下是小编给大家整理后的python提取内容关键词的方法，欢迎大家前来参阅。

python提取内容关键词的方法

篇1：python提取内容关键词的方法

作者：上大王字体：[增加减小] 类型：

这篇文章主要介绍了python提取内容关键词的方法,适用于英文关键词的提取,非常具有实用价值,需要的朋友可以参考下

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

# thetokenizer.com//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)

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，

篇2：python提取字典key列表的方法

作者：企鹅不笨字体：[增加减小] 类型：转载

具体如下：

这段代码可以把字典的所有key输出为一个数组

d2 = {‘spam‘: 2, ‘ham‘: 1, ‘eggs‘: 3} # make a dictionaryprint d2 # order is scrambledprint d2.keys() # create a new list of my keys

希望本文所述对大家的Python程序设计有所帮助。

篇3：python比较2个xml内容的方法

作者：像风一样的自由字体：[增加减小] 类型：

这篇文章主要介绍了python比较2个xml内容的方法,涉及Python操作XML文件的相关技巧,需要的朋友可以参考下

本文实例讲述了python比较2个xml内容的方法，分享给大家供大家参考。具体分析如下：

from xml.etree import ElementTree K=True main_pid = 10000 loop_depth = 0 def compare_xml(left, right, key_info=‘.‘): global loop_depth loop_depth += 1 if loop_depth == 1: print if left.tag != right.tag: print_diff(main_pid, key_info, ‘difftag‘, left.tag, right.tag) return if left.text != right.text: print_diff(main_pid, key_info, ‘difftext‘, left.text, right.text) return leftitems = dict(left.items) rightitems = dict(right.items()) for k,v in leftitems.items(): if k not in rightitems: s = ‘%s/%s‘ % (key_info, left.tag) print_diff(main_pid, s, ‘lostattr‘, k, “”) for k,v in rightitems.items(): if k not in leftitems: s = ‘%s/%s‘ % (key_info, right.tag) print_diff(main_pid, s, ‘extraattr‘, “”, k) leftnodes = left.getchildren() rightnodes = right.getchildren() leftlen = len(leftnodes) rightlen = len(rightnodes) if leftlen != rightlen: s = ‘%s/%s‘ % (key_info, right.tag) print_diff(main_pid, s, ‘difflen‘, leftlen, rightlen) return l = leftlen%-40s [ %s != %s ]‘%(msg.upper(), main_pid, key_info.strip(‘./‘), base_type, test_type) print info.encode(‘gbk‘) K = False

调用：

if __name__ == ‘__main__‘: s1 = ‘‘‘‘‘<?xml version=“1.0” encoding=“UTF-8”?>\ \\ linux\ 30\\\ windows\ 20\\‘‘‘ s2 = ‘‘‘‘‘<?xml version=“1.0” encoding=“UTF-8”?>\ \\ windows\ 20\\\ linux\ 30\\‘‘‘ lroot = ElementTree.fromstring(s1) rroot = ElementTree.fromstring(s2) compare_xml(lroot, rroot)

希望本文所述对大家的Python程序设计有所帮助，