Using temporal bursts for query modeling
Information Retrieval(2014)
摘要
We present an approach to query modeling that leverages the temporal distribution of documents in an initially retrieved set of documents. In news-related document collections such distributions tend to exhibit bursts. Here, we define a burst to be a time period where unusually many documents are published. In our approach we detect bursts in result lists returned for a query. We then model the term distributions of the bursts using a reduced result list and select its most descriptive terms. Finally, we merge the sets of terms obtained in this manner so as to arrive at a reformulation of the original query. For query sets that consist of both temporal and non-temporal queries, our query modeling approach incorporates an effective selection method of terms. We consistently and significantly improve over various baselines, such as relevance models, on both news collections and a collection of blog posts.
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关键词
Information retrieval,Temporal information retrieval,Query modeling
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