基于语义相关度主题爬虫的语料采集方法  

Corpus Collection Based on Semantic Relevancy Focused Crawler

作　　者：周昆 王钊 于碧辉 ZHOU Kun;WANG Zhao;YU Bi-Hui(University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;Center for Information Technology,Shenyang State Tax Bureau,Shenyang 110013,China)

机构地区：[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

出　　处：《计算机系统应用》2019年第5期190-195,共6页Computer Systems & Applications

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%.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%.

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 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]