{"title":"N个语义类比2个更难","authors":"Ben Carterette, R. Jones, W. Greiner, C. Barr","doi":"10.3115/1273073.1273080","DOIUrl":null,"url":null,"abstract":"We show that we can automatically classify semantically related phrases into 10 classes. Classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, HTML markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. Our work provides a benchmark for automatic n-way classification into WordNet's semantic classes, both on a TREC news corpus and on a corpus of substitutable search query phrases.","PeriodicalId":287679,"journal":{"name":"Proceedings of the COLING/ACL on Main conference poster sessions -","volume":"29 7","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"N Semantic Classes are Harder than Two\",\"authors\":\"Ben Carterette, R. Jones, W. Greiner, C. Barr\",\"doi\":\"10.3115/1273073.1273080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show that we can automatically classify semantically related phrases into 10 classes. Classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, HTML markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. Our work provides a benchmark for automatic n-way classification into WordNet's semantic classes, both on a TREC news corpus and on a corpus of substitutable search query phrases.\",\"PeriodicalId\":287679,\"journal\":{\"name\":\"Proceedings of the COLING/ACL on Main conference poster sessions -\",\"volume\":\"29 7\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the COLING/ACL on Main conference poster sessions -\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1273073.1273080\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the COLING/ACL on Main conference poster sessions -","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1273073.1273080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We show that we can automatically classify semantically related phrases into 10 classes. Classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, HTML markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. Our work provides a benchmark for automatic n-way classification into WordNet's semantic classes, both on a TREC news corpus and on a corpus of substitutable search query phrases.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
