Time-Aware Latent Concept Expansion for Microblog Search
Time-Aware Latent Concept Expansion for Microblog Search
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DOI：
10.1609/icwsm.v8i1.14519
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Taiki Miyanishi;Kazuhiro Seki;K. Uehara
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文献类型：
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作者：
Taiki Miyanishi;Kazuhiro Seki;K. Uehara
将单词的时间属性纳入基于相关性反馈的查询扩展方法已被证明对微博搜索具有显着的积极影响。与这种基于单词的查询扩展方法相比，我们提出了一种基于时间相关性模型的基于概念的查询扩展方法，该方法利用微博上概念（例如术语和短语）的时间变化。我们的模型通过跟踪概念频率随时间的变化自然地扩展了极其有效的现有基于概念的相关性模型。此外，所提出的模型产生了在与给定主题相关的特定时间段内频繁使用的重要概念，这比单词更好地区分相关和不相关的微博文档。我们使用微博数据语料库（Tweets2011语料库）的实验表明，所提出的基于概念的查询扩展方法显着提高了搜索性能，特别是对于高度相关的文档。
Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog search.In contrast to such word-based query expansion methods, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms and phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time.Moreover, the proposed model produces important concepts that are frequently used within a particular time periodassociated with a given topic, which better discriminate between relevant and non-relevant microblog documents than words.Our experiments using a corpus of microblog data (Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially for highly relevant documents.