使用外部数据源的流数据的主动查询扩展
Proactive Query Expansion for Streaming Data Using External Source
摘要：
查询扩展是通过添加相关词来重新表达原始查询的过程。为了提高查询扩展方法的性能或提高检索结果的质量，选择要添加哪些项是任何信息检索系统的一个重要方面。添加能够积极地影响搜索查询的质量或者信息量足够大的词在返回或收集覆盖特定主题的相关文档中起重要作用，这能够导致提高信息检索系统的效率。通常，使用查询扩展技术来向给定搜索查询添加或替换单词以收集相关数据。本文设计并实现了一个自动查询扩展流水线。我们概述了几种使用不同方法扩展查询的工具。我们的方法依赖于随着时间的推移在流数据中定位紧急事件，并使用概率主题模型从目标文档中找到隐藏的主题。我们使用动态特征向量中心性来触发紧急事件，并使用潜在狄利克雷分配来发现主题。此外，我们使用一个外部数据源作为辅助流，以补充与相关的词的主要流和扩展的查询使用的话从主要和辅助流。对2015年巴尔的摩抗议活动期间发生的事件相关的Twitter数据（主流）进行了实验研究。检索结果的质量是使用流数据的质量指标来衡量的：tweets count，hashtag count和hashtag clustering。
英文摘要：
Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can positively impact the quality of the search query or are informative enough play an important role in returning or gathering relevant documents that cover a certain topic can result in improving the efficiency of the information retrieval system. Typically, query expansion techniques are used to add or substitute words to a given search query to collect relevant data. In this paper, we design and implement a pipeline of automated query expansion. We outline several tools using different methods to expand the query. Our methods depend on targeting emergent events in streaming data over time and finding the hidden topics from targeted documents using probabilistic topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent events, and the Latent Dirichlet Allocation to discover the topics. Also, we use an external data source as a secondary stream to supplement the primary stream with relevant words and expand the query using the words from both primary and secondary streams. An experimental study is performed on Twitter data (primary stream) related to the events that happened during protests in Baltimore in 2015. The quality of the retrieved results was measured using a quality indicator of the streaming data: tweets count, hashtag count, and hashtag clustering.
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。
