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Query Modeling for Entity Search Based on Terms, Categories, and Examples

机译:基于术语,类别和示例的实体搜索查询建模

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摘要

Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.
机译:用户经常搜索实体而不是文档,并且在这种设置下,除了一系列查询词(例如类别信息和示例实体)之外,他们还愿意提供额外的输入。我们为实体搜索提出了一个通用的概率框架,以使用这些类型的输入进行查询建模的多种方式来评估和提供见解。我们专注于类别信息的使用,并展示了基于类别的表示优于基于术语的表示,并且还使用示例实体论证了基于类别的扩展的有效性。我们表现​​最好的模型在INEX-XER实体排名和列表完成任务上显示出非常有竞争力的表现。

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