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Refining Query Expansion Terms using Query Context

Published:11 December 2018Publication History

ABSTRACT

Query expansion is commonly used to combat the vocabulary mismatch problem, it bridges the disparity between the vocabulary used in the corpus and search queries. However, if expansion terms are not chosen carefully, there is a risk of including spurious expansion terms, which can broaden the potential interpretations of the modified query. Unintentionally increasing the semantic ambiguity in this way is known as query drift.

In this short paper we propose using the query context to inform the expansion term selection process. Using WordNet as an initial source of expansion terms, we refine the candidate expansions by discriminating relevancy. We found that our term selection process is more effective than the standard approach. Our technique targets terms which relate to the entire query as a whole, but predominately focuses on excluding spurious expansion terms. Both help reduce query drift and increase query performance.

References

  1. Jing Bai, Dawei Song, Peter Bruza, Jian-Yun Nie, and Guihong Cao. 2005. Query Expansion Using Term Relationships in Language Models for Information Retrieval. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM '05). ACM, New York, NY, USA, 688--695. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Claudio Carpineto, Renato de Mori, Giovanni Romano, and Brigitte Bigi. 2001. An Information-theoretic Approach to Automatic Query Expansion. ACM Trans. Inf. Syst. 19, 1 (Jan. 2001), 1--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Claudio Carpineto and Giovanni Romano. 2012. A Survey of Automatic Query Expansion in Information Retrieval. ACM Comput. Surv. 44, 1, Article 1 (Jan. 2012), 50 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Reuben Crimp and Andrew Trotman. 2017. Automatic Term Reweighting for Query Expansion. In Proceedings of the 22Nd Australasian Document Computing Symposium (ADCS 2017). ACM, New York, NY, USA, Article 3, 4 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Tamas E. Doszkocs. 1978. AID, An Associative Interactive Dictionary for Online Searching. Online Information Review 2 (12 1978), 163--173.Google ScholarGoogle Scholar
  6. David S. Johnson, Maria Minkoff, and Steven Phillips. 2000. The Prize Collecting Steiner Tree Problem: Theory and Practice. In Proceedings of the Eleventh Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '00). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 760--769. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Prashanti Manda and Todd Vision. 2018. An analysis and comparison of the statistical sensitivity of semantic similarity metrics. bioRxiv (2018).Google ScholarGoogle Scholar
  8. G. A. Miller. 1995. WordNet: A Lexical Database for English. CACM 38, 11 (1995), 39--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S.E. Robertson, S. Walker, S. Jones, M.M. Hancock-Beaulieu, and M. Gatford. 1996. Okapi at TREC-3. 109--126.Google ScholarGoogle Scholar
  10. S.E. Robertson. 1991. On Term Selection for Query Expansion. J. Doc. 46, 4 (Jan. 1991), 359--364. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. E. Robertson and Sparck J. K. 1976. Relevance Weighting of Search Terms. Journal of the American Society for Information Science (pre-1986) 27, 3 (May 1976), 129. Copyright - Copyright Wiley Periodicals Inc. May/Jun 1976; Last updated - 2010-06-09.Google ScholarGoogle Scholar
  12. J. J. Rocchio. 1971. Relevance feedback in information retrieval. In The Smart retrieval system - experiments in automatic document processing, G. Salton (Ed.). Englewood Cliffs, NJ: Prentice-Hall, 313--323.Google ScholarGoogle Scholar
  13. G. Salton and M. E. Lesk. 1968. Computer Evaluation of Indexing and Text Processing. J. ACM 15, 1 (1968), 8--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Trotman, C. L. A. Clarke, I. Ounis, S. Culpepper, M.-A. Cartright, and S. Geva. 2012. Open Source Information Retrieval: A Report on the SIGIR 2012 Workshop. SIGIR Forum 46, 2 (2012), 95--101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Trotman, A. Puurula, and B. Burgess. 2014. Improvements to BM25 and Language Models Examined. In ADCS '14. 58:58--58:65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. E. M. Voorhees. 1994. Query Expansion Using Lexical-semantic Relations. In SIGIR '94. 61--69. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Y.-C. Wang, J. Vandendorpe, and M. Evens. 1985. Relational thesauri in information retrieval. JASIS 36, 1 (1985), 15--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Zhibiao Wu and Martha Palmer. 1994. Verbs Semantics and Lexical Selection. In Proceedings of the 32Nd Annual Meeting on Association for Computational Linguistics (ACL '94). Association for Computational Linguistics, Stroudsburg, PA, USA, 133--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. L. Zhao and J. Callan. 2010. Term Necessity Prediction. In CIKM 2010. 259--268. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Other conferences
      ADCS '18: Proceedings of the 23rd Australasian Document Computing Symposium
      December 2018
      78 pages
      ISBN:9781450365499
      DOI:10.1145/3291992

      Copyright © 2018 ACM

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      Publication History

      • Published: 11 December 2018

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      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      ADCS '18 Paper Acceptance Rate13of20submissions,65%Overall Acceptance Rate30of57submissions,53%

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