Detection of Semantic Relationships between Terms with a New Statistical Method

Nesrine Ksentini, Mohamed Tmar, Faïez Gargouri

2014

Abstract

Semantic relatedness between terms plays an important role in many applications, such as information retrieval, in order to disambiguate document content. This latter is generally studied among pairs of terms and is usually presented in a non-linear way. This paper presents a new statistical method for detecting relationships between terms called Least Square Mehod which defines these relations linear and between a set of terms. The evaluation of the proposed method has led to optimal results with low error rate and meaningful relationships. Experimental results show that the use of these relationships in query expansion process improves the retrieval results.

References

  1. Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pas¸ca, M., and Soroa, A. (2009). A study on similarity and relatedness using distributional and wordnetbased approaches. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 7809, pages 19- 27, Stroudsburg, PA, USA. Association for Computational Linguistics.
  2. Agirre, E., Cuadros, M., Rigau, G., and Soroa, A. (2010). Exploring knowledge bases for similarity. In Chair), N. C. C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., and Tapias, D., editors, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
  3. Ahram, T. Z. (2008). Information retrieval performance enhancement using the average standard estimator and the multi-criteria decision weighted set of performance measures. PhD thesis, University of Central Florida Orlando, Florida.
  4. Hearst, M. (1998). WordNet: An electronic lexical database and some of its applications. In Fellbaum, C., editor, Automated Discovery of WordNet Relations. MIT Press.
  5. Imafouo, A. and Tannier, X. (2005). Retrieval status values in information retrieval evaluation. In String Processing and Information Retrieval, pages 224-227. Springer.
  6. Miller, S. J. (2006). The method of least squares.
  7. Ruiz-Casado, M., Alfonseca, E., and Castells, P. (2005). Using context-window overlapping in synonym discovery and ontology extension. In International Conference on Recent Advances in Natural Language Processing (RANLP 2005), Borovets, Bulgaria.
  8. Sahami, M. and Heilman, T. D. (2006). A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of the 15th international conference on World Wide Web, WWW 7806, pages 377- 386, New York, NY, USA. ACM.
  9. Sanderson, M. (2010). Test collection based evaluation of information retrieval systems. Now Publishers Inc.
  10. Turney, P. D. (2001). Mining the web for synonyms: Pmi-ir versus lsa on toefl. In Proceedings of the 12th European Conference on Machine Learning, EMCL 7801, pages 491-502, London, UK, UK. Springer-Verlag.
  11. Wasilewski, P. (2011). Query expansion by semantic modeling of information needs.
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Paper Citation


in Harvard Style

Ksentini N., Tmar M. and Gargouri F. (2014). Detection of Semantic Relationships between Terms with a New Statistical Method . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-024-6, pages 340-343. DOI: 10.5220/0004960403400343


in Bibtex Style

@conference{webist14,
author={Nesrine Ksentini and Mohamed Tmar and Faïez Gargouri},
title={Detection of Semantic Relationships between Terms with a New Statistical Method},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2014},
pages={340-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004960403400343},
isbn={978-989-758-024-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Detection of Semantic Relationships between Terms with a New Statistical Method
SN - 978-989-758-024-6
AU - Ksentini N.
AU - Tmar M.
AU - Gargouri F.
PY - 2014
SP - 340
EP - 343
DO - 10.5220/0004960403400343