{"title":"基于共聚类方法的动词同义词提取","authors":"Koichi Takeuchi","doi":"10.1109/ISUC.2008.66","DOIUrl":null,"url":null,"abstract":"This paper describes that a graph-based co-clustering approach is suitable for extraction of verb synonyms from large scale texts. The proposed bipartite graph algorithm can produce clusters of verb synonyms as well as noun synonyms taking into account word co-occurrence between verb and its argument. Experimental results show that the co-clustering approach achieve higher accuracy than those by a vector-based single clustering approach that are usually used for construction of thesaurus.","PeriodicalId":339811,"journal":{"name":"2008 Second International Symposium on Universal Communication","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Extraction of Verb Synonyms using Co-clustering Approach\",\"authors\":\"Koichi Takeuchi\",\"doi\":\"10.1109/ISUC.2008.66\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes that a graph-based co-clustering approach is suitable for extraction of verb synonyms from large scale texts. The proposed bipartite graph algorithm can produce clusters of verb synonyms as well as noun synonyms taking into account word co-occurrence between verb and its argument. Experimental results show that the co-clustering approach achieve higher accuracy than those by a vector-based single clustering approach that are usually used for construction of thesaurus.\",\"PeriodicalId\":339811,\"journal\":{\"name\":\"2008 Second International Symposium on Universal Communication\",\"volume\":\"106 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Second International Symposium on Universal Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISUC.2008.66\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Second International Symposium on Universal Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISUC.2008.66","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extraction of Verb Synonyms using Co-clustering Approach
This paper describes that a graph-based co-clustering approach is suitable for extraction of verb synonyms from large scale texts. The proposed bipartite graph algorithm can produce clusters of verb synonyms as well as noun synonyms taking into account word co-occurrence between verb and its argument. Experimental results show that the co-clustering approach achieve higher accuracy than those by a vector-based single clustering approach that are usually used for construction of thesaurus.