Yael Anava, Anna Shtok, Oren Kurland, Ella Rabinovich
{"title":"一个概率融合框架","authors":"Yael Anava, Anna Shtok, Oren Kurland, Ella Rabinovich","doi":"10.1145/2983323.2983739","DOIUrl":null,"url":null,"abstract":"There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A Probabilistic Fusion Framework\",\"authors\":\"Yael Anava, Anna Shtok, Oren Kurland, Ella Rabinovich\",\"doi\":\"10.1145/2983323.2983739\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.\",\"PeriodicalId\":250808,\"journal\":{\"name\":\"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2983323.2983739\",\"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 25th ACM International on Conference on Information and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2983323.2983739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.