Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Combining Semantic Query Disambiguation and Expansion to Improve Intelligent Information Retrieval

  • Conference paper
  • First Online:
Agents and Artificial Intelligence (ICAART 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8946))

Included in the following conference series:

Abstract

We show in this paper how Semantic Query Disambiguation (SQD) combined with Semantic Query Expansion (SQE) can improve the effectiveness of intelligent information retrieval. Firstly, we propose and assess a possibilistic-based approach mixing SQD and SQE. This approach is based on corpus analysis using co-occurrence graphs modeled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantage of a double measure (possibility and necessity). Secondly, we propose and evaluate a probabilistic circuit-based approach combining SQD and SQE in an intelligent information retrieval context. In this approach, both SQD and SQE tasks are based on a graph data model, in which circuits between its nodes (words) represent the probabilistic scores for their semantic proximities. In order to compare the performance of these two approaches, we perform our experiments using the standard ROMANSEVAL test collection for the SQD task and the CLEF-2003 benchmark for the SQE process in French monolingual information retrieval evaluation. The results show the impact of SQD on SQE based on the recall/precision standard metrics for both the possibilistic and the probabilistic circuit-based approaches. Besides, the results of the possibilistic approach outperform the probabilistic ones, since it takes into account of imprecision cases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Krovetz, R.: Homonymy and polysemy in information retrieval. In: Proceedings of the 8th Conference on European Chapter of the Association for Computational Linguistics, pp. 72–79. Association for Computational Linguistics, Stroudsburg, PA, USA (1997)

    Google Scholar 

  2. Paskalis, F.B.D., Khodra, M.L.: Word sense disambiguation in information retrieval using query expansion. In: International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1–6 (2011)

    Google Scholar 

  3. Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. (CSUR) 41, 1–69 (2009)

    Article  Google Scholar 

  4. Chan, Y.S., Ng, H.T.: Word sense disambiguation improves statistical machine translation. In: 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), pp. 33–40 (2007)

    Google Scholar 

  5. Carpuat, M., Wu, D.: Improving statistical machine translation using word sense disambiguation. In: The 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), pp. 61–72 (2007)

    Google Scholar 

  6. Chifu, A.-G., Ionescu, R.-T.: Word sense disambiguation to improve precision for ambiguous queries. Cent. Eur. J. Comput. Sci. 2, 398–411 (2012)

    MATH  Google Scholar 

  7. Krovetz, R., Croft, W.B.: Lexical ambiguity and information retrieval. ACM Trans. Inf. Syst. 10, 115–141 (1992)

    Article  Google Scholar 

  8. Voorhees, E.M.: Using WordNet to disambiguate word senses for text retrieval. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 171–180. ACM, New York, NY, USA (1993)

    Google Scholar 

  9. Schütze, H., Pedersen, J.O.: Information retrieval based on word senses (1995)

    Google Scholar 

  10. Gonzalo, J., Verdejo, F., Chugur, I., Cigarrin, J.: Indexing with WordNet synsets can improve text retrieval. In: Proceedings of the COLING-ACL Workshop on Usage of WordNet in Natural Language Processing Systems, pp. 38–44 (1998)

    Google Scholar 

  11. Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation in information retrieval revisited. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 159–166. ACM, New York, NY, USA (2003)

    Google Scholar 

  12. Kim, S., Seo, H., Rim, H.: Information retrieval using word senses: root sense tagging approach. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 258–265 (2004)

    Google Scholar 

  13. Liu, S., Yu, C., Meng, W.: Word sense disambiguation in queries. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 525–532. ACM, New York, NY, USA (2005)

    Google Scholar 

  14. Zhong, Z., Ng, H.T.: Word sense disambiguation improves information retrieval. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers – vol. 1, pp. 273–282. Association for Computational Linguistics, Stroudsburg, PA, USA (2012)

    Google Scholar 

  15. Elayeb, B., Bounhas, I., Ben Khiroun, O., Evrard, F., Bellamine-BenSaoud, N.: Towards a possibilistic information retrieval system using semantic query expansion. Int. J. Intell. Inf. Technol. 7, 1–25 (2011)

    Article  Google Scholar 

  16. Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. (CSUR) 44, 1–50 (2012)

    Article  MATH  Google Scholar 

  17. Ben Khiroun, O., Elayeb, B., Bounhas, I., Evrard, F., Bellamine-BenSaoud, N.: A possibilistic approach for automatic word sense disambiguation. In: Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING), pp. 261–275, Taiwan (2012)

    Google Scholar 

  18. Ben Khiroun, O., Elayeb, B., Bounhas, I., Evrard, F., Bellamine-BenSaoud, N.: Improving query expansion by automatic query disambiguation in intelligent information retrieval. In: The 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), pp. 153–160. Angers, Loire Valley, France (2014)

    Google Scholar 

  19. Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using WordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  20. Sanderson, M.: Word sense disambiguation and information retrieval. In: Croft, B.W., van Rijsbergen, C.J. (eds.) Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’94), pp. 142–151. Springer, New York (1994)

    Google Scholar 

  21. Sanderson, M.: Retrieving with good sense. Inf. Retr. 2, 49–69 (2000)

    Article  Google Scholar 

  22. Gonzalo, J., Peñas, A., Verdejo, F.: Lexical ambiguity and information retrieval revisited. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in NLP and Very Large Corpora (EMNLP/VLC), pp. 195–202 (1999)

    Google Scholar 

  23. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3, 235–244 (1990)

    Article  Google Scholar 

  24. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–281. ACM, New York, NY, USA (1998)

    Google Scholar 

  25. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  MATH  Google Scholar 

  26. Rocchio, J.: Relevance Feedback in Information Retrieval. The SMART Retrieval System, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

  27. Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Croft, B.W., van Rijsbergen, C.J. (eds.) Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’94), pp. 61–69. Springer, New York (1994)

    Google Scholar 

  28. Smeaton, A.F.: Using NLP or NLP resources for information retrieval tasks. In: Strzalkowski, T. (ed.) Natural Language Information Retrieval, pp. 99–111. Kluwer Academic Publishers, Dordrecht (1997)

    Google Scholar 

  29. Boughanem, M., Brini, A., Dubois, D.: Possibilistic networks for information retrieval. Int. J. Approx. Reason. 50, 957–968 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  30. Ben Khiroun, O., Elayeb, B., Bounhas, I., Evrard, F., Bellamine Ben Saoud, N.: A possibilistic approach for semantic query expansion. In: The 4th International Conference on Internet Technologies and Applications (ITA 2011), Wrexham Wales (UK), pp. 308–316 (2011)

    Google Scholar 

  31. Elayeb, B.: SARIPOD: Système multi-Agent de Recherche Intelligente POssibiliste de Documents Web. Ph.D. thesis, INP Toulouse France (2009)

    Google Scholar 

  32. Elayeb, B., Evrard, F., Zaghdoud, M., Ben Ahmed, M.: Towards an intelligent possibilistic web information retrieval using multiagent system. Interact. Technol. Smart Educ. (ITSE), Spec. Issue N. Learn. Support Syst. 6, 40–59 (2009)

    Google Scholar 

  33. Fang, H.: A re-examination of query expansion using lexical resources. In: Proceedings of ACL-08: HLT, pp. 139–147 (2008)

    Google Scholar 

  34. Cao, G., Nie, J.-Y., Bai, J.: Integrating word relationships into language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 298–305. ACM, New York, NY, USA (2005)

    Google Scholar 

  35. Agirre, E., Arregi, X., Otegi, A.: Document expansion based on WordNet for robust IR. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 9–17. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)

    Google Scholar 

  36. Pinto, F.J., Pérez-sanjulián, C.F.: Automatic query expansion and word sense disambiguation with long and short queries using WordNet under vector model. Actas de los Talleres de las Jornadas de Ingeniería del Software y Bases de Datos. 2, 17–23 (2008)

    Google Scholar 

  37. Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  38. Dubois, D., Prade, H.: Possibility theory and its application: where do we stand. Mathw. Soft Comput. 18, 18–31 (2011)

    Google Scholar 

  39. Dubois, D., Prade, H.: Possibility theory. In: Meyers, R.A. (ed.) Computational Complexity, pp. 2240–2252. Springer, New York (2012)

    Chapter  Google Scholar 

  40. Braschler, M., Peters, C.: CLEF 2003 methodology and metrics. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds.) CLEF 2003. LNCS, vol. 3237, pp. 7–20. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  41. Segond, F.: Framework and results for French. Comput. Humanit. 34, 49–60 (2000)

    Article  Google Scholar 

  42. Ounis, I., Lioma, C., Macdonald, C., Plachouras, V.: Research directions in terrier: a search engine for advanced retrieval on the web. CEPIS Upgrad. J. 8, 49–56 (2007)

    Google Scholar 

  43. Ogilvie, P., Voorhees, E., Callan, J.: On the number of terms used in automatic query expansion. Inf. Retr. 12, 666–679 (2009)

    Article  Google Scholar 

  44. Elayeb, B., Bounhas, I., Ben Khiroun, O., Evrard, F., Bellamine-BenSaoud, N.: A comparative study between possibilistic and probabilistic approaches for monolingual word sense disambiguation. Knowl. Inf. Syst. (2014). doi:10.1007/s10115-014-0753-z

    Google Scholar 

Download references

Acknowledgements

We are grateful to the Evaluations and Language resources Distribution Agency (ELDA) which kindly provided us the Le Monde 94 and ATS 94 document collections of the CLEF 2003 campaign.

Author information

Authors and Affiliations

  1. RIADI Research Laboratory, ENSI Manouba University, 2010, Manouba, Tunisia

    Bilel Elayeb, Oussama Ben Khiroun & Narjès Bellamine Ben Saoud

  2. LISI Laboratory of Computer Science for Industrial Systems, ISD Manouba University, 2010, Manouba, Tunisia

    Ibrahim Bounhas

  3. Emirates College of Technology, P.O. Box: 41009, Abu Dhabi, UAE

    Bilel Elayeb

  4. Higher Institute of Informatics (ISI), Tunis El Manar University, 1002, Tunis, Tunisia

    Narjès Bellamine Ben Saoud

Authors
  1. Bilel Elayeb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ibrahim Bounhas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oussama Ben Khiroun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Narjès Bellamine Ben Saoud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bilel Elayeb .

Editor information

Editors and Affiliations

  1. LERIA - UFR Sciences, Angers, France

    Béatrice Duval

  2. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  3. LERIA - UFR Sciences, Angers, France

    Stephane Loiseau

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Elayeb, B., Bounhas, I., Khiroun, O.B., Saoud, N.B.B. (2015). Combining Semantic Query Disambiguation and Expansion to Improve Intelligent Information Retrieval. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2014. Lecture Notes in Computer Science(), vol 8946. Springer, Cham. https://doi.org/10.1007/978-3-319-25210-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25210-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25209-4

  • Online ISBN: 978-3-319-25210-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation