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Statistical query expansion for sentence retrieval and its effects on weak and strong queries

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The retrieval of sentences that are relevant to a given information need is a challenging passage retrieval task. In this context, the well-known vocabulary mismatch problem arises severely because of the fine granularity of the task. Short queries, which are usually the rule rather than the exception, aggravate the problem. Consequently, effective sentence retrieval methods tend to apply some form of query expansion, usually based on pseudo-relevance feedback. Nevertheless, there are no extensive studies comparing different statistical expansion strategies for sentence retrieval. In this work we study thoroughly the effect of distinct statistical expansion methods on sentence retrieval. We start from a set of retrieved documents in which relevant sentences have to be found. In our experiments different term selection strategies are evaluated and we provide empirical evidence to show that expansion before sentence retrieval yields competitive performance. This is particularly novel because expansion for sentence retrieval is often done after sentence retrieval (i.e. expansion terms are mined from a ranked set of sentences) and there are no comparative results available between both types of expansion. Furthermore, this comparison is particularly valuable because there are important implications in time efficiency. We also carefully analyze expansion on weak and strong queries and demonstrate clearly that expanding queries before sentence retrieval is not only more convenient for efficiency purposes, but also more effective when handling poor queries.
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... Ko et al. [34] performed query expansion from assumed relevant sentences to generate snippet. Bando et al. [36] and Losada [35] in general used the same technique but obtained expansion terms from initially retrieved top documents [36]. Takamura and Okumura [30] formulated a summarization task as a maximum coverage problem. ...
... This is a novel summary that was generated using query expansion from related YA answers by applying LCA (Local Context Analysis) technique [35], [36], [47]. None of previous work has applied query expansion using external CQA resources for document summarization. ...
... We set N equals to 45 following the best setting of Bando et al [36]. Following [35], [36], the sentences are then ranked according to their similarity with respect to the expanded query using the sentence ranking method by Allan et al [48]. ...
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