Advertisement

Reproducing and Generalizing Semantic Term Matching in Axiomatic Information Retrieval

  • Peilin Yang
  • Jimmy LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

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.

Keywords

Axiomatic retrieval Query expansion 

Notes

Acknowledgments

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

References

  1. 1.
    Berger, A., Lafferty, J.: Information retrieval as statistical translation. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 222–229. SIGIR 1999. ACM, New York (1999).  https://doi.org/10.1145/312624.312681
  2. 2.
    Fang, H., Zhai, C.: An exploration of axiomatic approaches to information retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 480–487. SIGIR 2005. ACM, New York (2005).  https://doi.org/10.1145/1076034.1076116
  3. 3.
    Fang, H., Zhai, C.: Semantic term matching in axiomatic approaches to information retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 115–122. SIGIR 2006. ACM, New York (2006).  https://doi.org/10.1145/1148170.1148193
  4. 4.
    Lin, J., et al.: Toward reproducible baselines: the open-source IR reproducibility challenge. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 408–420. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30671-1_30CrossRefGoogle Scholar
  5. 5.
    Lin, J., Efron, M.: Overview of the TREC-2013 Microblog Track. In: Proceedings of the Twenty-Second Text REtrieval Conference (TREC 2013), Gaithersburg, Maryland (2013)Google Scholar
  6. 6.
    Onal, K.D., et al.: Neural information retrieval: at the end of the early years. Inf. Retrieval 21(2–3), 111–182 (2018).  https://doi.org/10.1007/s10791-017-9321-yCrossRefGoogle Scholar
  7. 7.
    Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System-Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  8. 8.
    Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 61–69. SIGIR 1994. ACM, New York (1994). http://dl.acm.org/citation.cfm?id=188490.188508CrossRefGoogle Scholar
  9. 9.
    Xu, J., Croft, W.B.: Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inf. Syst. 18(1), 79–112 (2000)CrossRefGoogle Scholar
  10. 10.
    Yang, P., Fang, H.: Evaluating the effectiveness of axiomatic approaches in web track. In: Proceedings of the Twenty-Second Text REtrieval Conference (TREC 2013), Gaithersburg, Maryland (2013)Google Scholar
  11. 11.
    Yang, P., Fang, H., Lin, J.: Anserini: enabling the use of Lucene for information retrieval research. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1253–1256. SIGIR 2017. ACM, New York (2017).  https://doi.org/10.1145/3077136.3080721
  12. 12.
    Yang, P., Fang, H., Lin, J.: Anserini: reproducible ranking baselines using Lucene. J. Data Inf. Qual. 10(4) (2018). Article 16CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.OntarioCanada
  2. 2.David R. Cheriton School of Computer ScienceUniversity of WaterlooOntarioCanada

Personalised recommendations