{"title":"自动同义词库建设","authors":"Dongqiang Yang, D. Powers","doi":"10.1145/1378279.1378304","DOIUrl":null,"url":null,"abstract":"In this paper we introduce a novel method of automating thesauri using syntactically constrained distributional similarity. With respect to syntactically conditioned cooccurrences, most popular approaches to automatic thesaurus construction simply ignore the salience of grammatical relations and effectively merge them into one united 'context'. We distinguish semantic differences of each syntactic dependency and propose to generate thesauri through word overlapping across major types of grammatical relations. The encouraging results show that our proposal can build automatic thesauri with significantly higher precision than the traditional methods.","PeriodicalId":136130,"journal":{"name":"Australasian Computer Science Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Automatic thesaurus construction\",\"authors\":\"Dongqiang Yang, D. Powers\",\"doi\":\"10.1145/1378279.1378304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce a novel method of automating thesauri using syntactically constrained distributional similarity. With respect to syntactically conditioned cooccurrences, most popular approaches to automatic thesaurus construction simply ignore the salience of grammatical relations and effectively merge them into one united 'context'. We distinguish semantic differences of each syntactic dependency and propose to generate thesauri through word overlapping across major types of grammatical relations. The encouraging results show that our proposal can build automatic thesauri with significantly higher precision than the traditional methods.\",\"PeriodicalId\":136130,\"journal\":{\"name\":\"Australasian Computer Science Conference\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Australasian Computer Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1378279.1378304\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australasian Computer Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1378279.1378304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we introduce a novel method of automating thesauri using syntactically constrained distributional similarity. With respect to syntactically conditioned cooccurrences, most popular approaches to automatic thesaurus construction simply ignore the salience of grammatical relations and effectively merge them into one united 'context'. We distinguish semantic differences of each syntactic dependency and propose to generate thesauri through word overlapping across major types of grammatical relations. The encouraging results show that our proposal can build automatic thesauri with significantly higher precision than the traditional methods.