{"title":"CoQEx: Entity Counts Explained","authors":"Shrestha Ghosh, S. Razniewski, G. Weikum","doi":"10.1145/3539597.3573021","DOIUrl":null,"url":null,"abstract":"For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg.de/.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3539597.3573021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg.de/.
对于开放域问答,关于实体计数的查询,比如印度尼西亚有多少种语言,是具有挑战性的。这样的查询可以通过简洁的上下文来回答:估计有700种语言,以及实例:爪哇语和巽他语。候选答案自然会产生一个分布,其中表示查询实体计数的计数上下文及其语义子组经常共存,而实例将计数置于其构成实体中。在这个演示中,我们展示了CoQEx方法(计数查询解释)[5,6],它聚合和结构跨搜索片段的解释性证据,以回答与实体计数相关的用户查询[4]。给定实体计数查询,我们的系统CoQEx检索搜索片段,并为用户提供分布感知的预测预测,将计数上下文分类为语义组,并根据计数对实例进行排名,所有这些都是实时的。我们的演示可以访问://nlcounqer.mpi- info .mpg.de/。