Bsmooth：在交互式数据检索中从用户反馈中学习以消除查询词的歧义
Bsmooth: Learning from user feedback to disambiguate query terms in interactive data retrieval
摘要：
人们对支持不精确查询（例如，关键字搜索或自然语言查询）。为了支持这样的查询，数据库系统通常需要使用其固有可用的任何资源（数据库模式、数据值分布、自然语言模型等）来消除用户指定的查询对数据库的部分的歧义。通常，系统也会有一个可用的用户交互日志，它可以作为一个外部资源，以补充他们的模型基于自己的内在资源。这导致了一个问题，如何最好地结合系统的先验排名与来自用户交互日志的洞察力。统计推断技术，如最大似然或贝叶斯更新从主观先验证明不适用于一个简单的方式，由于可能的噪音，从用户的搜索行为和编码偏见特有的系统的模型。在本文中，我们解决了这样的学习问题，在交互式数据检索，特别关注用户指定的查询条件的类型分类。提出了一种新的贝叶斯平滑算法Bsmooth，该算法具有简单、快速、灵活、准确的特点.我们分析建立了一些理想的属性，并显示，通过实验对一个独立的基准，这样的学习层的增加比标准方法执行得更好。
英文摘要：
There is great interest in supporting imprecise queries (e.g., keyword search or natural language queries) over databases today. To support such queries, the database system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available to it (the database schema, data values distributions, natural language models etc). Often, systems will also have a user-interaction log available, which can serve as an extrinsic resource to supplement their model based on their own intrinsic resources. This leads to a problem of how best to combine the system's prior ranking with insight derived from the user-interaction log. Statistical inference techniques such as maximum likelihood or Bayesian updates from a subjective prior turn out not to apply in a straightforward way due to possible noise from user search behavior and to encoding biases endemic to the system's models. In this paper, we address such learning problem in interactive data retrieval, with specific focus on type classification for user-specified query terms. We develop a novel Bayesian smoothing algorithm, Bsmooth, which is simple, fast, flexible and accurate. We analytically establish some desirable properties and show, through experiments against an independent benchmark, that the addition of such a learning layer performs much better than standard methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
