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Reproducing and Generalizing Semantic Term Matching in Axiomatic Information Retrieval

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

In the framework of axiomatic information retrieval, the semantic term matching technique proposed by Fang and Zhai in SIGIR 2006 has been shown to be effective in addressing the vocabulary mismatch problem, with experimental evidence provided from newswire collections. This paper reproduces and generalizes these results in Anserini, an open-source IR toolkit built on Lucene. In addition to making an implementation of axiomatic semantic term matching available on a widely-used open-source platform, we describe a series of experiments that help researchers and practitioners better understand its behavior across a number of test collections spanning newswire, web, and microblogs. Results show that axiomatic semantic term matching can be applied on top of different base retrieval models, and that its effectiveness varies across different document genres, each requiring different parameter settings for optimal effectiveness.

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Notes

  1. 1.

    https://www.acm.org/publications/policies/artifact-review-badging.

  2. 2.

    https://github.com/Peilin-Yang/axiomatic_query_expansion.

  3. 3.

    http://anserini.io/.

  4. 4.

    For the ClueWeb collections, we measured effectiveness in terms of NDCG@20, so the analysis for the top 50 and 100 documents are not applicable; nevertheless, we have included those results in the graphs for completeness.

  5. 5.

    This was accomplished by using 42 as the “meta seed” to generate a pseudo-random sequence of random seeds for each experimental run.

References

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Acknowledgments

This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.

Author information

Authors and Affiliations

  1. Ontario, Canada

    Peilin Yang

  2. David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada

    Jimmy Lin

Authors
  1. Peilin Yang
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  2. Jimmy Lin
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Corresponding author

Correspondence to Jimmy Lin .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Yang, P., Lin, J. (2019). Reproducing and Generalizing Semantic Term Matching in Axiomatic Information Retrieval. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_24

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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