{"title":"概率对话建模","authors":"Oliver Lemon, Prashant Parikh, S. Peters","doi":"10.3115/1118121.1118138","DOIUrl":null,"url":null,"abstract":"We show how Bayesian networks and related probabilistic methods provide an efficient way of capturing the complex balancing of different factors that determine interpretation and generation in dialogue. As a case study, we show how a probabilistic approach can be used to model anaphora resolution in dialogue.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Probabilistic Dialogue Modelling\",\"authors\":\"Oliver Lemon, Prashant Parikh, S. Peters\",\"doi\":\"10.3115/1118121.1118138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show how Bayesian networks and related probabilistic methods provide an efficient way of capturing the complex balancing of different factors that determine interpretation and generation in dialogue. As a case study, we show how a probabilistic approach can be used to model anaphora resolution in dialogue.\",\"PeriodicalId\":426429,\"journal\":{\"name\":\"SIGDIAL Workshop\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIGDIAL Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1118121.1118138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGDIAL Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1118121.1118138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We show how Bayesian networks and related probabilistic methods provide an efficient way of capturing the complex balancing of different factors that determine interpretation and generation in dialogue. As a case study, we show how a probabilistic approach can be used to model anaphora resolution in dialogue.
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