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Combining compound and single terms under language model framework

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Abstract

Most existing Information Retrieval model including probabilistic and vector space models are based on the term independence hypothesis. To go beyond this assumption and thereby capture the semantics of document and query more accurately, several works have incorporated phrases or other syntactic information in IR, such attempts have shown slight benefit, at best. Particularly in language modeling approaches this extension is achieved through the use of the bigram or n-gram models. However, in these models all bigrams/n-grams are considered and weighted uniformly. In this paper we introduce a new approach to select and weight relevant n-grams associated with a document. Experimental results on three TREC test collections showed an improvement over three strongest state-of-the-art model baselines, which are the original unigram language model, the Markov Random Field model, and the positional language model.

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Acknowledgments

We thank the editor and anonymous reviewers for their very useful comments and suggestions.

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Authors and Affiliations

  1. Departement of Computer science, University of Mouloud Mammeri, 15000 , Tizi-Ouzou, Algeria

    Arezki Hammache

  2. LARI Laboratory, Departement of Computer Science, University of Mouloud Mammeri, 1500 , Tizi-Ouzou, Algeria

    Rachid Ahmed-Ouamer

  3. IRIT Laboratory, University of Paul Sabatier, 118 route de Narbonne, 31062 , Toulouse Cedex 09, France

    Mohand Boughanem

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  1. Arezki Hammache
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Correspondence to Arezki Hammache.

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Hammache, A., Boughanem, M. & Ahmed-Ouamer, R. Combining compound and single terms under language model framework. Knowl Inf Syst 39, 329–349 (2014). https://doi.org/10.1007/s10115-013-0618-x

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  • DOI: https://doi.org/10.1007/s10115-013-0618-x

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