Skip to main content
SpringerLink
Log in
Book cover

Transactions on Computational Collective Intelligence XXIX pp 123–143Cite as

WordNet and Wiktionary-Based Approach for Word Sense Disambiguation

  • Chapter
  • First Online:

  • 464 Accesses

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 10840))

Abstract

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach W3SD (This paper is an extended version of our work [4] published in the 8th International Conference on Computational Collective Intelligence.) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using WordNet and Wiktionary features through the taxonomic information content from WordNet and the glosses from Wiktionary. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks.

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

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

Learn about institutional subscriptions

Notes

  1. 1.

    http://babelfy.org.

  2. 2.

    http://babelnet.org/.

  3. 3.

    https://en.wiktionary.org/wiki/Wiktionary:Main_Page.

  4. 4.

    http://nlp.stanford.edu/software/.

  5. 5.

    http://wnetss-api.smr-team.org/.

  6. 6.

    http://projects.csail.mit.edu/jsemcor/.

  7. 7.

    www.hipposmond.com/senseval2/Results/all_graphs.xls.

References

  1. Weaver, W.: Translation. In: Locke, W.N., Boothe, A.D. (eds.) Machine Translation of Languages, pp. 15–23. MIT Press, Cambridge (1949)

    Google Scholar 

  2. Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist. 21, 543–565 (1995)

    MathSciNet  Google Scholar 

  3. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database (Language, Speech, and Communication), illustrated edn. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  4. Ben Aouicha, M., Hadj Taieb, M.A., Ibn Marai, H.: WSD-TIC: word sense disambiguation using taxonomic information content. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9875, pp. 131–142. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45243-2_12

    Chapter  Google Scholar 

  5. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284, 34–43 (2001)

    Article  Google Scholar 

  6. 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, Jeju Island (2012)

    Google Scholar 

  7. Ng, H.T.: Does word sense disambiguation improve information retrieval? In: Proceedings of the Fourth Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR 2011, Glasgow, United Kingdom, 28 October 2011, pp. 17–18 (2011)

    Google Scholar 

  8. Guyot, J., Falquet, G., Radhouani, S., Benzineb, K.: Analysis of word sense disambiguation-based information retrieval. In: Evaluating Systems for Multilingual and Multimodal Information Access: 9th Workshop of the Cross-language Evaluation Forum, CLEF 2008, Aarhus, Denmark, 17–19 September 2008, Revised Selected Papers, pp. 146–154 (2008)

    Google Scholar 

  9. Sanderson, M.: Word sense disambiguation and information retrieval. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 142–151. Springer, London (1994). https://doi.org/10.1007/978-1-4471-2099-5_15

    Chapter  Google Scholar 

  10. 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, Toronto (2003)

    Google Scholar 

  11. Carpuat, M., Wu, D.: Word sense disambiguation vs. statistical machine translation. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 387–394. Association for Computational Linguistics, Ann Arbor (2005)

    Google Scholar 

  12. Carpuat, M., Wu, D.: How phrase sense disambiguation outperforms word sense disambiguation for statistical machine translation. In: Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation (TMI) (2007)

    Google Scholar 

  13. Neale, S., Gomes, L., Agirre, E., de Lacalle, O.L., Branco, A.: Word sense-aware machine translation: including senses as contextual features for improved translation models. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, Portorož, Slovenia, 23–28 May 2016 (2016)

    Google Scholar 

  14. Liu, Y., Scheuermann, P., Li, X., Zhu, X.: Using WordNet to disambiguate word senses for text classification. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 781–789. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72588-6_127

    Chapter  Google Scholar 

  15. Schadd, F.C., Roos, N.: Word-sense disambiguation for ontology mapping: concept disambiguation using virtual documents and information retrieval techniques. J. Data Semant. 4, 167–186 (2015)

    Article  Google Scholar 

  16. Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining learning and word sense disambiguation for intelligent user profiling. In: Twentieth International Joint Conference on Artificial Intelligence, 6–12 January 2007, Hyderabad, India (2007, to appear)

    Google Scholar 

  17. Che, W., Liu, T.: Using word sense disambiguation for semantic role labeling. In: 4th International Universal Communication Symposium, IUCS 2010, Beijing, China, 18–19 October 2010, pp. 167–174 (2010)

    Google Scholar 

  18. Kulkarni, A., Heilman, M., Eskénazi, M., Callan, J.: Word sense disambiguation for vocabulary learning. In: Proceedings of the 9th International Conference Intelligent Tutoring Systems, ITS 2008, Montreal, Canada, 23–27 June 2008, pp. 500–509 (2008)

    Google Scholar 

  19. Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. TACL 2, 231–244 (2014)

    Google Scholar 

  20. Véronis, J.: HyperLex: lexical cartography for information retrieval. Comput. Speech Lang. 18, 223–252 (2004)

    Article  Google Scholar 

  21. Nameh, M., Fakhrahmad, S.M., Jahromi, M.Z.: A new approach to word sense disambiguation based on context similarity. In: Proceedings of the Workshop of the World Congress on Engineering (2011)

    Google Scholar 

  22. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26. ACM, Toronto (1986)

    Google Scholar 

  23. Banerjee, S., Pedersen, T.: Extended gloss overlaps as a measure of semantic relatedness. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 805–810. Morgan Kaufmann Publishers Inc., Acapulco (2003)

    Google Scholar 

  24. Sinha, R., Mihalcea, R.: Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In: Proceedings of the International Conference on Semantic Computing, pp. 363–369. IEEE Computer Society, Washington, DC (2007)

    Google Scholar 

  25. Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. CoRR cmp-lg/9709008 (1997)

    Google Scholar 

  26. Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellfaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283. MIT Press, Cambridge (1998)

    Google Scholar 

  27. López-Arévalo, I., Sosa, V.J.S., López, F.R., Tello-Leal, E.: Improving selection of synsets from WordNet for domain-specific word sense disambiguation. Comput. Speech Lang. 41, 128–145 (2017)

    Article  Google Scholar 

  28. Panchenko, A., Faralli, S., Ponzetto, S.P., Biemann, C.: Using linked disambiguated distributional networks for word sense disambiguation. In: EACL 2017 Workshop on Sense, Concept and Entity Representations and their Applications: Proceedings of the Workshop, 4 April 2017, Valencia, Spain, pp. 72–78. Association for Computational Linguistics, Stroudsburg (2017)

    Google Scholar 

  29. Faralli, S., Panchenko, A., Biemann, C., Ponzetto, S.P.: Linked disambiguated distributional semantic networks. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 56–64. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_7

    Chapter  Google Scholar 

  30. Rothe, S., Schütze, H.: AutoExtend: extending word embeddings to embeddings for synsets and lexemes. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, 26–31 July 2015, Beijing, China, Long Papers, vol. 1, pp. 1793–1803 (2015)

    Google Scholar 

  31. Hessami, E., Mahmoudi, F., Jadidinejad, A.H.: Unsupervised graph-based word sense disambiguation using lexical relation of WordNet. Int. J. Comput. Sci. Issues 8(6), 225–230 (2011). No. 3

    Google Scholar 

  32. Basile, P., Caputo, A., Semeraro, G.: An enhanced lesk word sense disambiguation algorithm through a distributional semantic model. In: COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 23–29 August 2014, Dublin, Ireland, pp. 1591–1600 (2014)

    Google Scholar 

  33. Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)

    Article  MathSciNet  Google Scholar 

  34. Zesch, T., Müller, C., Gurevych, I.: Extracting lexical semantic knowledge from Wikipedia and Wiktionary. In: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, 26 May–1 June 2008, Marrakech, Morocco (2008)

    Google Scholar 

  35. Hadj Taieb, M.A., Ben Aouicha, M., Ben Hamadou, A.: A new semantic relatedness measurement using WordNet features. Knowl. Inf. Syst. 41, 467–497 (2014)

    Article  Google Scholar 

  36. Ben Aouicha, M., Hadj Taieb, M.A., Ben Hamadou, A.: SISR: system for integrating semantic relatedness and similarity measures. Soft. Comput. 22, 1855–1879 (2016)

    Article  Google Scholar 

  37. Didion, J.: JWNL (Java WordNet Library) (2004)

    Google Scholar 

  38. Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41, 10:1–10:69 (2009)

    Article  Google Scholar 

  39. Palmer, M., Fellbaum, C., Cotton, S., Delfs, L., Dang, H.T.: English tasks: all-words and verb lexical sample. In: Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems, pp. 21–24. Association for Computational Linguistics, Toulouse (2001)

    Google Scholar 

  40. McCarthy, D., Carroll, J., Preiss, J.: Disambiguating noun and verb senses using automatically acquired selectional preferences. In: The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems, pp. 119–122. Association for Computational Linguistics, Toulouse (2001)

    Google Scholar 

  41. Fernández-Amorós, D., Gonzalo, J., Verdejo, F.: The UNED systems at Senseval-2. In: The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems, pp. 75–78. Association for Computational Linguistics, Toulouse (2001)

    Google Scholar 

  42. Kenneth, C.L.: Use of machine readable dictionaries for word-sense disambiguation in SENSEVAL-2. In: The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems, pp. 75–78. Association for Computational Linguistics, Toulouse (2001)

    Google Scholar 

  43. Snyder, B., Palmer, M.: The English all-words task. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 41–43. Association for Computational Linguistics, Barcelona (2004)

    Google Scholar 

  44. Mccarthy, D., Koeling, R., Weeds, J., Carroll, J.: Using automatically acquired predominant senses for word sense disambiguation. In: Proceedings of the ACL SENSEVAL-3 Workshop, pp. 151–154 (2004)

    Google Scholar 

  45. Strapparava, C., Gliozzo, A., Giuliano, C.: Pattern abstraction and term similarity for word sense disambiguation: IRST at Senseval-3. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 229–234. Association for Computational Linguistics, Barcelona (2004)

    Google Scholar 

  46. Seo, H.-C., Rim, H.-C., Kim, S.-H.: KUNLP system in Senseval-3. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 222–225. Association for Computational Linguistics, Barcelona (2004)

    Google Scholar 

  47. Buscaldi, D., Rosso, P., Masulli, F.: The upv-unige-CIAOSENSO WSD system. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 77–82. Association for Computational Linguistics, Barcelona (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia InfoRmation System and Advanced Computing Laboratory, Sfax University, Sfax, Tunisia

    Mohamed Ben Aouicha & Mohamed Ali Hadj Taieb

  2. Faculty of Sciences, Sfax University, Sfax, Tunisia

    Hania Ibn Marai

Authors
  1. Mohamed Ben Aouicha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ali Hadj Taieb
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hania Ibn Marai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Ben Aouicha .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ben Aouicha, M., Hadj Taieb, M.A., Ibn Marai, H. (2018). WordNet and Wiktionary-Based Approach for Word Sense Disambiguation. In: Nguyen, N., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXIX. Lecture Notes in Computer Science(), vol 10840. Springer, Cham. https://doi.org/10.1007/978-3-319-90287-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90287-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90286-9

  • Online ISBN: 978-3-319-90287-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation