Extending the language modeling framework for sentence retrieval to include local context |
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| Authors: | Email author" target="_blank">Ronald?T?FernándezEmail author David?E?Losada Leif?A?Azzopardi |
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| Institution: | (1) Grupo de Sistemas Inteligentes, Universidad de Santiago de Compostela, Santiago de Compostela, Spain;(2) Department of Computing Science, University of Glasgow, Glasgow, UK |
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| Abstract: | Employing effective methods of sentence retrieval is essential for many tasks in Information Retrieval, such as summarization,
novelty detection and question answering. The best performing sentence retrieval techniques attempt to perform matching directly
between the sentences and the query. However, in this paper, we posit that the local context of a sentence can provide crucial
additional evidence to further improve sentence retrieval. Using a Language Modeling Framework, we propose a novel reformulation
of the sentence retrieval problem that extends previous approaches so that the local context is seamlessly incorporated within
the retrieval models. In a series of comprehensive experiments, we show that localized smoothing and the prior importance
of a sentence can improve retrieval effectiveness. The proposed models significantly and substantially outperform the state
of the art and other competitive sentence retrieval baselines on recall-oriented measures, while remaining competitive on
precision-oriented measures. This research demonstrates that local context plays an important role in estimating the relevance
of a sentence, and that existing sentence retrieval language models can be extended to utilize this evidence effectively. |
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