###

计算机系统应用英文版:2019,28(5):190-195

View/Add Comment 过刊浏览高级检索 HTML

←前一篇 | 后一篇→

码上扫一扫！

下载全文

基于语义相关度主题爬虫的语料采集方法

周昆^1,2, 王钊³, 于碧辉^1,2

(1.中国科学院大学, 北京 100049;2.中国科学院沈阳计算技术研究所, 沈阳 110168;3.沈阳市国家税务局信息中心, 辽宁沈阳 110013)

Corpus Collection Based on Semantic Relevancy Focused Crawler

ZHOU Kun^1,2, WANG Zhao³, YU Bi-Hui^1,2

(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.Center for Information Technology, Shenyang State Tax Bureau, Shenyang 110013, China)

摘要

图/表

参考文献

相似文献

本文已被：浏览 1434次下载 1709次
Received:November 26, 2018 Revised:December 18, 2018

中文摘要: 针对特定领域语料采集任务，设计了基于语义相关度主题爬虫的语料采集方法.根据选定的主题词，利用页面描述信息，基于维基百科中文语料训练出的词分布式表示综合HowNet计算页面信息相关度，结合URL的结构信息预测未访问URL链指的页面内容与特定领域的相关程度.实验表明，系统能够有效的采集互联网中的党建领域页面内容作为党建领域生语料，在党建领域网站上的平均准确率达到94.87%，在门户网站上的平均准确率达到64.20%.

中文关键词: 生语料采集语义相关度主题爬虫页面信息相关度 URL结构信息

Abstract:To address the corpus collection, the corpus collection system based on semantic relevancy focused crawler is implemented. Word vector trained by Wikipedia and HowNet are used for calculating page information semantic relevancy with descriptive information according to topical keywords, and the URL structural information is used for calculating the topical relevancy. Experimental results show that this system has better effect on party-construction corpus collection with high precision of average accurate rate 94.87%, while the average accurate rate for web pages is 64.20%.

keywords: corpus collection semantic relevancy focused crawler page information semantic relevancy URL structural information

文章编号： 中图分类号： 文献标志码：

基金项目:

引用文本：
周昆,王钊,于碧辉.基于语义相关度主题爬虫的语料采集方法.计算机系统应用,2019,28(5):190-195
ZHOU Kun,WANG Zhao,YU Bi-Hui.Corpus Collection Based on Semantic Relevancy Focused Crawler.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):190-195

Author Name	Affiliation	E-mail
ZHOU Kun	University of Chinese Academy of Sciences, Beijing 100049, China Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China	982892502@qq.com
WANG Zhao	Center for Information Technology, Shenyang State Tax Bureau, Shenyang 110013, China
YU Bi-Hui	University of Chinese Academy of Sciences, Beijing 100049, China Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China

Author Name	Affiliation	E-mail
ZHOU Kun	University of Chinese Academy of Sciences, Beijing 100049, China Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China	982892502@qq.com
WANG Zhao	Center for Information Technology, Shenyang State Tax Bureau, Shenyang 110013, China
YU Bi-Hui	University of Chinese Academy of Sciences, Beijing 100049, China Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China