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Snippet Generation by Identifying Attribute Associated Information

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8281)

Abstract

In this paper, we focus on the task of using a web search engine to find the entity that best fits the user’s demand by comparing multiple entities of the same type. We call this task attribute-oriented entity search. As the primary task, we tackle the snippet generation problem. When users access a web search engine to locate entities, they input two kinds of queries; namely, type query and attribute query. Type query represents entity type. Attribute query represents specific entity attributes. We propose a method that generates snippets containing information associated with both type and attribute queries. Specifically, our model is an extension of the conventional query-biased summarization method, which consists of two probabilistic models. Our method introduces a novel probabilistic model, the ambiguous relevance model, to reflect the information about input attribute queries, which are written in a variety of words, in the generated snippet. The results of experiments show that our method can generate better snippets in terms of information about attribute queries than conventional methods while matching the performance of conventional methods with respect to information about type queries.

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Tanaka, Y., Suhara, Y., Hiroshima, N., Toda, H., Susaki, S. (2013). Snippet Generation by Identifying Attribute Associated Information. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-45068-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45067-9

  • Online ISBN: 978-3-642-45068-6

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