{"title":"基于随机游走模型的维基百科领域相关术语提取","authors":"Wenjuan Wu, Tao Liu, H. Hu, Xiaoyong Du","doi":"10.1109/CHINAGRID.2012.20","DOIUrl":null,"url":null,"abstract":"In this paper we present a new approach for the automatic identification of domain-relevant concepts and entities of a given domain using the category and page structures of the Wikipedia in a language independent way. By applying Markov random walk algorithm on the weighted Wikipedia link graph, our approach can identify large quantities of domain-relevant concepts and entities with very little human effort. Experimental results show that our method achieves high accuracy and acceptable efficiency in domain-relevant term extraction.","PeriodicalId":371382,"journal":{"name":"2012 Seventh ChinaGrid Annual Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Extracting Domain-Relevant Term Using Wikipedia Based on Random Walk Model\",\"authors\":\"Wenjuan Wu, Tao Liu, H. Hu, Xiaoyong Du\",\"doi\":\"10.1109/CHINAGRID.2012.20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a new approach for the automatic identification of domain-relevant concepts and entities of a given domain using the category and page structures of the Wikipedia in a language independent way. By applying Markov random walk algorithm on the weighted Wikipedia link graph, our approach can identify large quantities of domain-relevant concepts and entities with very little human effort. Experimental results show that our method achieves high accuracy and acceptable efficiency in domain-relevant term extraction.\",\"PeriodicalId\":371382,\"journal\":{\"name\":\"2012 Seventh ChinaGrid Annual Conference\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Seventh ChinaGrid Annual Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CHINAGRID.2012.20\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Seventh ChinaGrid Annual Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CHINAGRID.2012.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extracting Domain-Relevant Term Using Wikipedia Based on Random Walk Model
In this paper we present a new approach for the automatic identification of domain-relevant concepts and entities of a given domain using the category and page structures of the Wikipedia in a language independent way. By applying Markov random walk algorithm on the weighted Wikipedia link graph, our approach can identify large quantities of domain-relevant concepts and entities with very little human effort. Experimental results show that our method achieves high accuracy and acceptable efficiency in domain-relevant term extraction.