MEDI 2014: Model and Data Engineering pp 100-112 | Cite as

Leveraging Concepts and Semantic Relationships for Language Model Based Document Retrieval

  • Lynda Said Lhadj
  • Mohand Boughanem
  • Karima Amrouche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8748)

Abstract

During the last decades, many language models approaches have been proposed to alleviate the assumption of single term independency in documents. This assumption leads to two known problems in information retrieval, namely polysemy and synonymy. In this paper, we propose a new language model based on concepts, to answer the polysemy issue, and semantic dependencies, to handle the synonymy problem. Our purpose is to relax the independency constraint by representing documents and queries by their concepts instead of single words. We consider that a concept could be a single word, a frequent collocation in the corpus or an ontology entry. In addition, semantic dependencies between query and document concepts have been incorporated into our model using a semantic smoothing technique. This allows retrieving not only documents containing the same words with the query but also documents dealing with the same concepts. Experiments carried out on TREC collections showed that our model achieves significant results compared to a strong single term based model, namely uni-gram language model.

Keywords

Information Retrieval Language Modeling semantic smoothing Concept Semantic Relatonships 

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References

  1. 1.
    Bai, J., Song, D., Bruza, P., Nie, J.Y., Cao, G.: 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 2005, pp. 688–695. ACM (2005)Google Scholar
  2. 2.
    Banerjee, S., Pedersen, T.: The design, implementation, and use of the ngram statistics package. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 370–381. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Bao, S., Zhang, L., Chen, E., Long, M., Li, R., Yu, Y.: LSM: Language sense model for information retrieval. In: Yu, J.X., Kitsuregawa, M., Leong, H.-V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 97–108. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Baziz, M., Boughanem, M., Passi, G., Prade, H.: An information retrieval driven by ontology from query to document expansion. In: Large Scale Semantic Access to Content (Text, Image, Video, and Sound), RIAO 2007, pp. 301–313 (2007)Google Scholar
  5. 5.
    Bendersky, M., Croft, W.B.: Modeling higher-order term dependencies in information retrieval using query hypergraphs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 941–950. ACM (2012)Google Scholar
  6. 6.
    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, SIGIR 1999, pp. 222–229. ACM (1999)Google Scholar
  7. 7.
    Boughanem, M., Mallak, I., Prade, H.: A new factor for computing the relevance of a document to a query. In: Proceedings of the International Conference on Fuzzy Systems, pp. 1–6. IEEE (2010)Google Scholar
  8. 8.
    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, SIGIR 2005, pp. 298–305. ACM (2005)Google Scholar
  9. 9.
    Gao, J., Nie, J.Y., Wu, G., Cao, G.: Dependence language model for information retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 170–177. ACM (2004)Google Scholar
  10. 10.
    Hammache, A., Boughanem, M., Ahmed Ouamar, R.: Combining compound and single terms under language model framework. In: Knowledge and Information Systems, pp. 329–349 (2013)Google Scholar
  11. 11.
    Miller, G.A.: Wordnet: A lexical database for english. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  12. 12.
    Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Johnson, D.: Terrier information retrieval platform. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 517–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    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, SIGIR 1998, pp. 275–281. ACM (1998)Google Scholar
  14. 14.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, pp. 448–453. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
  15. 15.
    Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in wordnet. In: ECAI, vol. 4, pp. 1089–1090 (2004)Google Scholar
  16. 16.
    Song, F., Croft, W.B.: A general language model for information retrieval. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, CIKM 1999, pp. 316–321. ACM (1999)Google Scholar
  17. 17.
    Srikanth, M., Srihari, R.: Biterm language models for document retrieval. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2002, pp. 425–426. ACM (2002)Google Scholar
  18. 18.
    Srikanth, M., Srihari, R.: Incorporating query term dependencies in language models for document retrieval. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 405–406. ACM (2003)Google Scholar
  19. 19.
    Tu, X., He, T., Chen, L., Luo, J., Zhang, M.: Wikipedia-based semantic smoothing for the language modeling approach to information retrieval. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 370–381. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Victor, L., Croft, W.B.: Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 120–127. ACM (2001)Google Scholar
  21. 21.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 334–342. ACM (2001)Google Scholar
  22. 22.
    Zhang, W., Liu, S., Yu, C., Sun, C., Liu, F., Meng, W.: Recognition and classification of noun phrases in queries for effective retrieval. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 711–720. ACM (2007)Google Scholar
  23. 23.
    Zhou, X., Hu, X., Zhang, X.: Topic signature language models for ad hoc retrieval. IEEE Trans. on Knowl. and Data Eng. 19(9), 1276–1287 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lynda Said Lhadj
    • 1
  • Mohand Boughanem
    • 2
  • Karima Amrouche
    • 1
  1. 1.National High School for Computer Science (ESI)AlgiersAlgeria
  2. 2.IRITToulouse Cedex 9France

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