Skip to main content

A comparative study between possibilistic and probabilistic approaches for monolingual word sense disambiguation

Abstract

This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Notes

  1. http://www.lpl.univ-aix.fr/projects/romanseval.

  2. http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/.

  3. http://wt.jrc.it/lt/Acquis/.

  4. http://www.natcorp.ox.ac.uk/.

  5. http://americannationalcorpus.org/.

References

  1. Agirre E, Edmonds P (ed) (2006) Word sense disambiguation. algorithms and applications (Text, Speech and Language Technology). Springer, Dordrecht

  2. Agirre E, Martinez D (2000) Exploring automatic word sense disambiguation with decision lists and the Web. In: Buitelaar P, Hasida K (eds) Proceedings of the COLING 2000 workshop on semantic annotation and intelligent content. International Committee on Computational Linguistics, Luxembourg, pp 11–19

    Google Scholar 

  3. Agirre E, Soroa A (2009) Personalizing PageRank for word sense disambiguation. In: Lascarides A, Gardent C, Nivre J (eds) Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics. The Association for Computer Linguistics, Athens, Greece, pp 33–41

    Google Scholar 

  4. Agirre E, Lopez de Lacalle O, Soroa A (2009) Knowledge-based WSD and specific domains: performing better than generic supervised WSD. In: Boutilier C (ed) Proceedings of the 21st international joint conference on artificial intelligence. Pasadena, California, USA, pp 1501–1506

    Google Scholar 

  5. Apidianaki M (2009) Data-driven semantic analysis for multilingual WSD and lexical selection in translation. In: Lascarides A, Gardent C, Nivre J (eds) Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics. The Association for Computer Linguistics, Athens, Greece, pp 77–85

    Google Scholar 

  6. Audibert L (2002) Etude des critères de désambiguïsation sémantique automatique: Présentation et premiers résultats sur les cooccurrences. TALN-RECITAL-2002. Sixième Rencontre des Etudiants Chercheurs en Informatique pour le Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Nancy, France, pp 415–424

  7. Audibert L (2003a) Outils d’exploration de corpus et désambiguïsation lexicale automatique. Ph.D. Thesis, Université d’Aix-Marseille I - Université de Provence, France

  8. Audibert L (2003b) Etudes des critères de désambiguïsation sémantique automatique : résultat sur les cooccurrences. In : TALN-2003, Actes de la conférence Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Batz-sur-Mer, France, pp 35–44

  9. Ayed R, Bounhas I, Elayeb B, Evrard F, Bellamine Ben Saoud N (2012a) Arabic morphological analysis and disambiguation using a possibilistic classifier. In: Huang D, Ma J, Jo K-H et al (eds) Intelligent computing theories and applications—8th international conference. Springer, Berlin, Heidelberg, LNAI 7390, Huangshan, China, pp 274–279

  10. Ayed R, Bounhas I, Elayeb B, Evrard F, Bellamine Ben Saoud N (2012b) A possibilistic approach for the automatic morphological disambiguation of Arabic texts. In: Hochin T, Lee R. Y (eds) Proceedings of the 13th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing. IEEE Computer Society, Kyoto, Japan, pp 187–194.

  11. Ayed R, Bounhas I, Elayeb B, Bellamine Ben Saoud N, Evrard F (2014a) Evaluation d’une approche possibiliste pour la désambiguïsation des textes arabes. In: TALN-2014, Actes de la conférence Traitement Automatique des Langues. Association pour le, Traitement Automatique des Langues, 1–4 July 2014, Marseille, France

  12. Ayed R, Bounhas I, Elayeb B, Bellamine Ben Saoud N, Evrard F (2014b) Improving Arabic Texts morphological disambiguation using possibilistic classifier. In: Proceedings of the 19th international conference on application of natural language to information systems. Springer, Berlin, Germany. 18–20 June 2014, Montpellier, France

  13. Baldwin T, Su NK, Bond F, Fujita S, Martinez D, Tanaka T (2008) MRD-based word sense disambiguation: further extending lesk. In: IJCNLP 2008, proceedings of the 3rd international joint conference on natural language processing. The Association for Computer Linguistics, Hyderabad, India, pp 775–780

  14. Barathi M, Valli S (2010) Ontology based query expansion using word sense disambiguation. Int J Comput Sci Inf Secur 7(2):22–27

    Google Scholar 

  15. Barque L, Chaumartin F R (2008) La polysémie régulière dans WordNet. In: TALN-2008, Actes de la conférence Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Avignon, France. http://www.atala.org/taln_archives/TALN/TALN-2008/taln-2008-long-021.pdf

  16. Ben Khiroun O, Elayeb B, Bounhas I, Evrard F, Bellamine Ben Saoud N (2012) A possibilistic approach for automatic word sense disambiguation. In: Proceedings of the 24th conference on computational linguistics and speech processing. Association for Computational Linguistics and Chinese Language Processing, Chung-Li, Taiwan, pp 261–275

  17. Ben Khiroun O, Elayeb B, Bounhas I, Evrard F, Bellamine Ben Saoud N (2014) Improving query expansion by automatic query disambiguation in intelligent information retrieval. In: Filipe J, Fred ALN (eds) Proceedings of the 6th international conference on agents and artificial intelligence. SciTePress, Angers, Loire Valley, France, pp 153–160

  18. Benferhat S, Dubois D, Garcia L, Prade H (1999) Possibilistic logic bases and possibilistic graphs. In: Laskey KB, Prade H (eds) Proceedings of the fifteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann, Stockholm, Sweeden, pp 57–64

    Google Scholar 

  19. Ben Romdhane W, Elayeb B, Bounhas I, Evrard F, Bellamine Ben Saoud N (2013) A possibilistic query translation approach for cross-language information retrieval. In: Huang D, Jo K-H, Zhou Y-Q et al (eds) Intelligent computing theories and technology—9th international conference. Springer, Berlin, LNCS 7996, Nanning, China, pp 73–82

  20. Bookman LA (1987) A microfeature-based scheme for modelling semantics. In: McDermott JP (ed) Proceedings of the 10th international joint conference on artificial intelligence. Morgan Kaufmann, Milan, Italy, pp 611–614

    Google Scholar 

  21. Borgelt C, Gebhardt J, Kruse R (2000) Possibilistic Graphical Models. Computational intelligence in data mining, CISM courses and lectures 408:51–68

    Article  Google Scholar 

  22. Boughanem M, Brini A, Dubois D (2009) Possibilistic networks for information retrieval. Int J Approx Reason 50(7):957–968

    MATH  MathSciNet  Article  Google Scholar 

  23. Bounhas M, Mellouli K, Prade H, Serrurier M (2013) Possibilistic Classifiers for numerical data. Soft Comput 17(5):733–751

    Article  Google Scholar 

  24. Bounhas M, Ghasemi MH, Prade H, Serrurier M, Mellouli K (2014) Naïve possibilistic classifiers for imprecise or uncertain numerical data. Fuzzy Set Syst 239:137–156

    Article  Google Scholar 

  25. Brown PF, Pietra SAD, Pietra VJD, Mercer RL (1991) Word-sense disambiguation using statistical methods. In: Proceedings of the 29th annual meeting of the Association for Computational Linguistics. The Association for Computational Linguistics, Berkeley, California, USA, pp 264–270

  26. Brown SW, Dligach D, Palmer M (2011) Verbnet class assignment as a WSD task. In: Proceedings of the 9th international conference on computational semantics. The Association for Computational Linguistics, Stroudsburg, PA, USA, pp 85–94

  27. Brun C (2000) A client/server architecture for word sense disambiguation. In: Proceedings of the 18th international conference on Computational Linguistics. Morgan Kaufmann, Saarbrücken, Germany, pp 132–138

  28. Brun C, Jacquemin B, Segond F (2001) Exploitation de dictionnaires électroniques pour la désambiguïsation sémantique lexicale. Traitement Automatique de la Langue 42(3):667–691

    Google Scholar 

  29. Carpuat M, Wu D (2007) Improving statistical machine translation using word sense disambiguation. In: EMNLP-CoNLL 2007, Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning. Prague, Czech Republic, The Association for Computational Linguistics, pp 61–72

  30. Chan YS, Ng HT, Chiang D (2007) Word sense disambiguation improves statistical machine translation. In: Carroll JA, Bosch AVD, Zaenen A (eds) Proceedings of the 45th annual meeting of the Association for Computational Linguistics. Czech Republic, The Association for Computational Linguistics, Prague, pp 33–40

    Google Scholar 

  31. Chan YS, Ng HT (2005) Scaling up word sense disambiguation via parallel texts. In: Veloso MM, Kambhampati S (eds) Proceedings of The 20th national conference on artificial intelligence and the seventeenth innovative applications of artificial intelligence conference. AAAI Press/The MIT Press, Pittsburgh, Pennsylvania, USA, pp 1037–1042

  32. Clough P, Stevenson M (2004) Cross-language information retrieval using EuroWordNet and word sense disambiguation. In: McDonald S, Tait J (eds) Advances in information retrieval, 26th European conference on IR research. Springer, LNCS 2997, Sunderland, UK, pp 327–337

  33. Cohen J (1968) Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol Bull 70(4):213–220

    Article  Google Scholar 

  34. Cohn T (2003) Performance metrics for word sense disambiguation. In: Proceedings of the Australasian language technology workshop. The Australasian Language Technology Association, Melbourne, Australia, pp 86–93

  35. Dahlgren K (ed) (1988) Naive semantics for natural language understanding. Kluwer, Boston

    MATH  Google Scholar 

  36. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MATH  MathSciNet  Google Scholar 

  37. Diab M (2004) Word sense disambiguation within a multilingual framework. Ph.D. thesis, University of Maryland, USA

  38. Doddington GR, Mitchell A, Przybocki MA, Ramshaw LA, Strassel S, Weischedel RM (2004) The Automatic Content Extraction (ACE) program—tasks, data, and evaluation. Proceedings of the 4th international conference on language resources and evaluation. European Language Resources Association, Lisbonne, Portugale, pp 837–840

  39. Dubois D, Prade H (eds) (1988) Possibility theory: an approach to computerized processing. Plenum Press, New York

    MATH  Google Scholar 

  40. Dubois D, Prade H (2006) Représentations formelles de l’incertain et de l’imprécis. In: Bouyssou D, Dubois D, Pirlot M, Prade H (eds) Concepts et méthodes pour l’aide à la décision—outils de modélisation. Lavoisier, Paris, pp 111–171

    Google Scholar 

  41. Dubois D, Prade H (2009) Formal representations of uncertainty. In: Bouyssou D, Dubois D, Pirlot M, Prade H (eds) Decision-making process: concepts and methods. Wiley-ISTE, Hoboken

    Google Scholar 

  42. Dubois D, Prade H (eds) (1987) Théorie des possibilités: application à la représentation des connaissances en, Informatique edn. MASSON, Paris

    Google Scholar 

  43. Dubois D, Prade H (eds) (1988) Possibility theory. Plenum Press, New York

    MATH  Google Scholar 

  44. Dubois D, Prade H (1998) Possibility theory: qualitative and quantitative aspects. In: Gabbay DM, Smets P (eds) Quantified representation of uncertainty and imprecision. Klower, The Netherlands, Handbook of Defeasible Reasoning and Uncertainty Management Systems, pp 169–226

  45. Dubois D, Prade H (2000) An overview of ordinal and numerical approaches to causal diagnostic problem solving. In: Gabbay DM, Kruse R (eds) Abductive reasoning and learning. Handbooks of defeasible reasoning and uncertainty management systems, Klower, The Netherlands, pp 231–280

  46. Edmonds P, Hirst G (2002) Near-synonymy and lexical choice. Comput Linguist 28(2):105–144

    Article  Google Scholar 

  47. Elayeb B (2009) SARIPOD: Système multi-agent de recherche intelligente possibiliste des documents web. Ph.D. thesis, The National Polytechnic Institute of Toulouse, France—The National School of Computer Science, Manouba University, Tunisia

  48. Elayeb B, Bounhas I, Ben Khiroun O, Evrard F, Bellamine Ben Saoud N (2011) Towards a possibilistic information retrieval system using semantic query expansion. Int J Intell Inf Technol 7(4):1–25

  49. Elayeb B, Evrard F, Zaghdoud M, Ben Ahmed M (2009) Towards an intelligent possibilistic web information retrieval using multiagent system. Int Technol Smart Educ 6(1):40–59

    Article  Google Scholar 

  50. Erk K, Strapparava C (eds) (2010) Proceedings of the 5th international workshop on semantic evaluation. The Association for Computational Linguistics, Uppsala, Sweden

  51. Eugenio BD (2000) On the usage of Kappa to evaluate agreement on coding tasks. In: LREC 2000—Proceedings of the second international conference on language resources and evaluation. European Language Resources Association, Athens, Greece, pp 441–444

  52. Faralli S, Navigli R (2012) A new minimally-supervised framework for domain word sense disambiguation. In: EMNLP-CoNLL 2012—Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. The Association for Computational Linguistics, Jeju Island, Korea, pp 1411–1422

  53. Fellbaum C (ed) (1998) WordNet: an electronic lexical database. MIT Press, Cambridge

    MATH  Google Scholar 

  54. Gale WA, Church KW (1993) A program for aligning sentences in bilingual corpora. Comput Linguist 19(1):75–102

    Google Scholar 

  55. Gaume B (2004) Balades aléatoires dans les petits mondes lexicaux. Inf Int Intell 4(2):39–96

    Google Scholar 

  56. Gaume B (2006) Cartographier la forme du sens dans les petits mondes Lexicaux. In: Viprey J-M (ed) Proceedings of the 8th international conference on textual data and statistical analysis. Presses Universitaires de Franche-Comté, Besançon, France, pp 541–465

  57. Gaume B, Hathout N, Muller P (2004) Word sense disambiguation using a dictionary for sens similarity measure. COLING 2004—Proceedings of the 20th International conference on computational linguistics. The Association for Computational Linguistics, Geneva, Switzerland, pp 1194–1200

  58. Grishman R, Sundheim B (1996) Message understanding conference 6—a brief history. COLING 1996—Proceedinds of the 16th international conference on computational linguistics. The Association for Computational Linguistics, Copenhagen, Denmark pp 466–471

  59. Guthrie J, Guthrie L, Wilks Y, Aidinejad H (1991) Subject-dependent cooccurrence and word sense disambiguation. In: Proceedings of the 29th annual meeting of the Association for Computational Linguistics. The Association for Computational Linguistics, Berkeley, California, USA pp 146–152

  60. Haouari B, Ben Amor N, Elouedi Z, Mellouli K (2009) Naïve possibilistic network classifiers. Fuzzy Sets Syst 160(22):3224–3238

    MATH  MathSciNet  Article  Google Scholar 

  61. Ide N, Véronis J (1998) Word sense disambiguation: the state of the art. Comput Linguist 24:1–40

    Google Scholar 

  62. Jaynes ETh (ed) (2003) Probability theory: the logic of science. Cambridge University Press, Cambridge

    Google Scholar 

  63. Jimeno-Yepes AJ, McInnes BT, Aronson AR (2011) Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation. BMC Bioinform 12:223

    Article  Google Scholar 

  64. Khemakhem A, Gargouri B and Ben Hamadou A (2013) Collaborative enrichment of electronic dictionaries standardized-LMF. In: Métais E, Meziane F, Saraee M et al (eds) Natural language processing and information systems—18th international conference on applications of natural language to information systems. Springer, LNCS 7934, Salford, UK, pp 328–336

  65. Kilgarriff A (1994) The myth of completness and some problems with consistency (the role of frequency in deciding what goes in the dictionary). In: Martin W, Meijs W, Moerland M et al (eds) Proceedings of The 6th EURALEX international congress on lexicography. European Association for Lexicography, Amsterdam, The Netherlands pp 101–106

  66. Koeling R, McCarthy D, Carroll J (2005) Domain-specific sense distributions and predominant sense acquisition. In: Proceedings of the HLT/EMNLP 2005—human language technology conference and conference on empirical methods in natural language processing. The Association for Computational Linguistics, Vancouver, British Columbia, Canada, pp 419–426

  67. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the fourteenth international joint conference on artificial intelligence. Morgan Kaufmann, Montréal Québec, Canada, pp 1137–1143

  68. Krippendorff K (ed) (1980) Content analysis: an introduction to its methodology. Sage Publications, Beverly Hills

    Google Scholar 

  69. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174

    MATH  MathSciNet  Article  Google Scholar 

  70. Lefever E, Hoste V (2010) SemEval-2010 Task 3: cross-lingual word sense disambiguation. In: Proceedings of the 5th international workshop on semantic evaluation. The Association for Computational Linguistics, Uppsala, Sweden, pp 15–20

  71. Lefever E, Hoste V (2013) SemEval-2013 Task 10: cross-lingual word sense disambiguation. In: Second joint conference on lexical and computational semantics, Volume 2: Proceedings of the 7th international workshop on semantic evaluation. The Association for Computational Linguistics, Atlanta, Georgia, USA, pp 158–166

  72. Lesk M (1986) 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. ACM, Toronto, Ontario, Canada, pp 24–26

  73. Loper E, Yi S-T, Palmer M (2007) Combining lexical resources: mapping between PropBank and VerbNet. In: Proceedings of the 7th international workshop on computational semantics. Springer, Tlburg, The Netherlands. http://verbs.colorado.edu/~kipper/Papers/semlink_iwcs7.pdf

  74. Loupy C (2000) Evaluation de l’apport de connaissances linguistiques en désambiguïsation sémantique et recherche documentaire. Ph.D. thesis, Université d’Avignon, Avignon, France

  75. Masterman M (1961) Semantic message detection for machine translation, using an interlangua. In: Proceedings of the international conference on machine translation of languages and applied language analysis. National Physical Laboratory, Teddington, UK, pp 438–474

  76. McRoy S (1992) Using multiple knowledge sources for word sense discrimination. Comput Linguist 18:1–30

    Google Scholar 

  77. Mihalcea R, Moldovan D (1998) Word sense disambiguation based on semantic density. In: Proceedings of Coling-ACL’98 workshop—usage of wordnet in natural language processing systems. Montreal, Quebec, Canada, pp 16–22

  78. Navigli R (2009) Word sense disambiguation: a survey. ACM Comput Surv 41(2):1–69

    Article  Google Scholar 

  79. Navigli R, Lapata M (2007) Graph connectivity measures for unsupervised word sense disambiguation. In: Veloso MM (ed) Proceedings of the 20th international joint conference on artificial intelligence. Hyderabad, India, pp 1683–1688

  80. Navigli R, Ponzetto SP (2012) Joining forces pays off: multilingual joint word sense disambiguation. EMNLP-CoNLL 2012—Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. The Association for Computational Linguistics, Jeju Island, Korea, pp 1399–1410

  81. Ng HT, Wang B, Chan YS (2003) Exploiting parallel texts for word sense disambiguation: an empirical study. In: Proceedings of the 41st annual meeting of the association for computational linguistics. The Association for Computational Linguistics, Sapporo, Japan, pp 455–462

  82. Nguyen K-H, Ock Ch-Y (2013) Word sense disambiguation as a traveling salesman problem. Artif Intell Rev 40(4):405–427

    Article  Google Scholar 

  83. Ploux S, Victorri B (1998) Construction d’espaces sémantiques à l’aide de dictionnaires de synonymes. Traitement automatique des langues 39(1):161–182

    Google Scholar 

  84. Pilehvar MT, Jurgens D, Navigli R (2013) Align, disambiguate and walk: a unified approach for measuring semantic similarity. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics. The Association for Computational Linguistics, Sofia, Bulgaria, pp 1341–1351

  85. Ponzetto SP, Navigli R (2010) Knowledge-rich word sense disambiguation rivaling supervised systems. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics. The Association for Computational Linguistics, Uppsala, Sweden, pp 1522–1531

  86. Resnik P (1995) Disambiguating noun groupings with repect to WordNet senses. In: Yarowsky D, Church K (eds) Proceedings of the third workshop on very large Corpora. The Association for Computational Linguistics, Cambridge, pp 54–68

  87. Reymond D (2001) Dictionnaires distributionnels et étiquetage lexical de corpus. In: TALN-RECITAL-2001. Cinquième Rencontre des Etudiants Chercheurs en Informatique pour le Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Tours, France pp 473–482

  88. Reymond D (2002) Méthodologie pour la création d’un dictionnaire distributionnel dans une perspective d’étiquetage lexical semi-automatique. In: TALN-RECITAL-2002. Sixième Rencontre des Etudiants Chercheurs en Informatique pour le Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Nancy, France, pp 405–414

  89. Rijsbergen CJ, Keith V (eds) (2004) The geometry of information retrieval. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  90. Roche E, Schabes Y (eds) (1997) Finite-state language processing. MIT Press, Cambridge

    Google Scholar 

  91. Segond F (2000) Framework and results for French. Comput Humanit 34(1):49–60

    Article  Google Scholar 

  92. Sinha R, Mihalcea R (2007) Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In: Proceedings of the first IEEE international conference on semantic computing. IEEE Computer Society, Irvine, CA, USA pp 363–369

  93. Small S, Rieger C (1982) Parsing and comprehencing with word experts (a theory and its realization). In: Wendy L, Martin R (eds) Strategies for natural language processing. Lawrence Erlbaum Associates, Hillsdale, NJ, USA, pp 89–147

  94. Soto A, Olivas JA, Prieto ME (2008) Fuzzy approach of synonymy and polysemy for information retrieval. In: Bello R et al (eds) Granular computing: at the junction of rough sets and fuzzy sets studies in fuzziness and soft computing, vol 224, pp 179–198

  95. Specia L, Nunes MGV, Stevenson M (2007), Learning expressive models for word sense disambiguation. In: Proceedings of the 45th Annual meeting of the association of computational linguistics. Prague, Czech Republic, The Association of, Computational Linguistics, pp 41–48

  96. Stevenson M, Wilks Y (2001) The interaction of knowledge sources in word sense disambiguation. Comput Linguist 27(3):321–349

    Article  Google Scholar 

  97. Tae-Gil N, Park S-B, Lee S-J (2010) Unsupervised word sense disambiguation in biomedical texts with co-occurrence network and graph kernel. In: Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics. ACM, Toronto, Canada, pp 61–64

  98. Tlili-Guiassa Y, Merouani HF (2007) Désambiguïsation sémantique d’un texte Arabe. In: Proceedings of Séminaire national sur le langage naturel et l’intelligence artificielle. Chlef, Algérie, pp 41–60

  99. Tufis D, Ion R, Ide N (2004) Fine-grained word sense disambiguation based on parallel Corpora, Word Alignment, Word Clustering and Aligned WordNets. In: COLING 2004—Proceedings of the 20 th international conference on computational linguistics. The Association for Computational Linguistics, Geneva, Switzerland, pp 1312–1318

  100. Véronis J, Ide N (1990) Word sense disambiguation with very large neural networks extracted from machine readable dictionaries. In: COLING 1990—Proceedings of the 13th international conference on computational linguistics. The Association for Computational Linguistics, Helsinki, Finland, pp 389–394

  101. Véronis J (1998) A study of polysemy judgements and inter-annotator agreement. In: Programme and advanced papers of the Senseval workshop. Herstmonceux Castle, England, pp 2–4

  102. Véronis J (2003a) Sense tagging: does it make sense? In: Rayson P, Wilson A, McEnery T et al (eds.) Proceedings of the Corpus Linguistics 2001 conference. Peter Lang Frankfurt, Lancaster, UK. http://sites.univ-provence.fr/veronis/pdf/2001-lancaster-sense.pdf

  103. Véronis J (2003) Hyperlex: cartographie lexicale pour la recherche d’informations. In: TALN-2003. Actes de la conférence Traitement Automatique des Langues. Association pour le Traitement Automatique des Langues, Batz-sur-Mer, France, pp 265–274

  104. Vidhu Bhala R V and Abirami S (2012) Trends in word sense disambiguation. Artif Intell Rev, pp 1–13. doi:10.1007/s10462-012-9331-5

  105. Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Family Med 37(5):360–363

    Google Scholar 

  106. Voorhees EM (2004) Overview of the TREC-2004 question answering track. In: Proceedings of 13th text REtrieval conference. NIST Special Publication, pp 500–261, Gaithersburg, USA. http://trec.nist.gov/pubs/trec13/papers/QA.OVERVIEW.pdf

  107. Voorhees EM, Buckland LP (eds) (2006) Proceedings of the fifteenth text retrieval conference. NIST Special Publication 500-272, Gaithersburg, USA

  108. Vossen P (ed) (1998) EuroWordNet: a multilingual database with lexical semantic networks. Kluwer, Norwell

    MATH  Google Scholar 

  109. Waltz D, Pollack J (1985) Massively parallel parsing: a strongly interactive model of natural language interpretation. Cogn Sci 9(1):51–74

    Article  Google Scholar 

  110. Weiss SF (1973) Learning to disambiguate. Inf Storage Retr 9(1):33–41

    Article  Google Scholar 

  111. Wilks YA, Stevenson M (1997a) Combining independent knowledge source for word sense disambiguation. In: Proceedings of conference of recent advances in natural language processing. Tzigov Chark, Bulgaria, pp 1–7

  112. Wilks YA, Stevenson M (1997b) The grammar of sense: using part-of-speech tags as first step in semantic disambiguation. J Nat Lang Eng 4(3):135–143

    Google Scholar 

  113. Wilks Y (1975) Preference semantics. In: Keenan EL (ed) Formal Semantics of Natural Language. Cambridge University Press, Cambridge, pp 329–348

    Chapter  Google Scholar 

  114. Wilks YA, Fass DC, Guo CM, MacDonald JE, Plate T, Slator BA (eds) (1990) Providing machine tractable dictionary tools. MIT Press, Cambridge

    Google Scholar 

  115. Yarowsky D (2000) Hierarchical decision list for word sense disambiguation. Comput Humanit 34(1–2):179–186

    Article  Google Scholar 

  116. Yarowsky D, Cucerzan S, Florian R, Schafer C, Wicentowski R (2001) The johns hopkins SENSEVAL2 system descriptions. In: Preiss J, Yarowsky D (eds) Proceedings of The 2nd international workshop on evaluating word sense disambiguation systems. The Association for Computational Linguistics, Toulouse, France, pp 163–166

  117. Yuret D, Yatbaz MA (2010) The noisy channel model for unsupervised word sense disambiguation. Comput Linguist 36(1):111–127

    Article  Google Scholar 

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

    MATH  MathSciNet  Article  Google Scholar 

  119. Zhong Z, Ng HT (2010) It makes sense: a wide-coverage word sense disambiguation system for free text. In: Proceedings of the 48th annual meeting of the association for computational linguistics. The Association for Computational Linguistics, Uppsala, Sweden, pp 78–83

  120. Zhou X, Han H (2005) Survey of word sense disambiguation approaches. In: Russell I, Markov Z (eds) Proceedings of the eighteenth international florida artificial intelligence research society conference. AAAI Press, Clearwater Beach, pp 307–313

    Google Scholar 

  121. Zouaghi A, Merhbene L, Zrigui M (2012) Combination of information retrieval methods with LESK algorithm for Arabic word sense disambiguation. Artif Intell Rev 38(4):257–269

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to the anonymous reviewers whose relevant comments and convincing remarks helped us improve the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bilel Elayeb.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Elayeb, B., Bounhas, I., Ben Khiroun, O. et al. A comparative study between possibilistic and probabilistic approaches for monolingual word sense disambiguation. Knowl Inf Syst 44, 91–126 (2015). https://doi.org/10.1007/s10115-014-0753-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-014-0753-z

Keywords

  • Word sense disambiguation
  • Possibility theory
  • Probability theory
  • Semantic dictionary of contexts
  • Semantic graph