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International Symposium on String Processing and Information Retrieval

SPIRE 2019: String Processing and Information Retrieval pp 65–79Cite as

BM25 Beyond Query-Document Similarity

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11811))

Abstract

The massive growth of information produced and shared online has made retrieving relevant documents a difficult task. Query Expansion (QE) based on term co-occurrence statistics has been widely applied in an attempt to improve retrieval effectiveness. However, selecting good expansion terms using co-occurrence graphs is challenging. In this paper, we present an adapted version of the BM25 model, which allows measuring the similarity between terms. First, a context window-based approach is applied over the entire corpus in order to construct the term co-occurrence graph. Afterward, using the proposed adapted version of BM25, candidate expansion terms are selected according to their similarity with the whole query. This measure stands out by its ability to evaluate the discriminative power of terms and select semantically related terms to the query. Experiments on two ad-hoc TREC collections (the standard Robust04 collection and the new TREC Washington Post collection) show that our proposal outperforms the baselines over three state-of-the-art IR models and leads to significant improvements in retrieval effectiveness.

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Notes

  1. 1.

    https://trec.nist.gov/data/cd45/.

  2. 2.

    https://trec.nist.gov/data/wapost/.

  3. 3.

    http://terrier.org/.

  4. 4.

    We used the CBOW implementation of word2vec and we set the vectors dimension to 300.

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Author information

Authors and Affiliations

  1. LISI Laboratory of Computer Science for Industrial System, INSAT, Carthage University, Tunis, Tunisia

    Billel Aklouche, Ibrahim Bounhas & Yahya Slimani

  2. National School of Computer Science (ENSI), La Manouba University, Manouba, Tunisia

    Billel Aklouche

  3. Higher Institute of Multimedia Arts of Manouba (ISAMM), La Manouba University, Manouba, Tunisia

    Yahya Slimani

  4. JARIR: Joint group for Artificial Reasoning and Information Retrieval, Manouba, Tunisia

    Billel Aklouche, Ibrahim Bounhas & Yahya Slimani

Authors
  1. Billel Aklouche
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  2. Ibrahim Bounhas
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  3. Yahya Slimani
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Corresponding author

Correspondence to Billel Aklouche .

Editor information

Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Nieves R. Brisaboa

  2. University of Helsinki, Helsinki, Finland

    Simon J. Puglisi

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Aklouche, B., Bounhas, I., Slimani, Y. (2019). BM25 Beyond Query-Document Similarity. In: Brisaboa, N., Puglisi, S. (eds) String Processing and Information Retrieval. SPIRE 2019. Lecture Notes in Computer Science(), vol 11811. Springer, Cham. https://doi.org/10.1007/978-3-030-32686-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-32686-9_5

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  • Online ISBN: 978-3-030-32686-9

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