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Word sense disambiguation as a traveling salesman problem

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Abstract

Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.

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References

  • Agirre E, Soroa A (2009) Personalizing PageRank for word sense disambiguation. In: Proceedings of the 12th conference of the European chapter of the ACL (EACL 2009). Association for Computational Linguistics, Athens, pp 33–41

  • Agirre E et al (2010) SemEval-2010 task 17: all-words word sense disambiguation on a specific domain. In: Proceedings of the 5th international workshop on semantic evaluation. Association for Computational Linguistics, Uppsala, pp 75–80

  • Banerjee S, Pedersen T (2002) An adapted lesk algorithm for word sense disambiguation using WordNet. In: Proceedings of the 3rd international conference on computational linguistics and intelligent text processing. CICLing ’02. Springer, London, pp 136–145

  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7): 107–117

    Article  Google Scholar 

  • Buckley C (1985) Implementation of the SMART information retrieval system. Cornell University, Ithaca

    Google Scholar 

  • Budanitsky A, Hirst G (2006) Evaluating WordNet-based measures of lexical semantic relatedness. Comput Linguist 32(1): 13–47

    Article  MATH  Google Scholar 

  • Chan YS, Ng HT, Zhong Z (2007) NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks. In: Proceedings of the 4th international workshop on semantic evaluations (SemEval-2007). Association for Computational Linguistics, Prague, pp 253–256

  • Cottrell GW (1989) A connectionist approach to word sense disambiguation. Morgan Kaufmann Publishers Inc, San Francisco

    Google Scholar 

  • Cowie J, Guthrie J, Guthrie L (1992) Lexical disambiguation using simulated annealing. In: Proceedings of the workshop on speech and natural language. Association for Computational Linguistics, HLT ’91. Stroudsburg, pp 238–242

  • Decadt B et al (2004) GAMBL, genetic algorithm optimization of memory-based WSD. In: Mihalcea R, Edmonds P (eds) Senseval-3: 3rd international workshop on the evaluation of systems for the semantic analysis of text. Association for Computational Linguistics, Barcelona, pp 108–112

  • Dorigo M, Gambardella LM (1997) Ant colonies for the traveling salesman problem

  • Edmonds P, Cotton S (2002) SENSEVAL-2: overview. In: Proceedings of SENSEVAL-2: 2nd international workshop on evaluating word sense disambiguation systems, 5–6 July 2001, Toulouse, pp 1–7

  • Gelbukh AF, Sidorov G, Han S-Y (2005) On some optimization heuristics for lesk-like WSD algorithms. In: NLDB, pp 402–405

  • Hatori J, Miyao Y, Tsujii J (2008) Word sense disambiguation for all words using tree-structured conditional random fields. In: Coling 2008: companion volume: posters. Coling 2008. Organizing Committee, Manchester, pp 43–46

  • Hirst GJ (1984) Semantic interpretation against ambiguity. Brown University, Providence

    Google Scholar 

  • Ide N, Véronis J (1998) Introduction to the special issue on word sense disambiguation: the state of the art. Comput Linguist 24(1): 2–40

    Google Scholar 

  • Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: International conference research on computational linguistics (ROCLING X), pp 19–33

  • Kleinberg JM (1999) Hubs, authorities, and communities. ACM Comput Surv 31(4es):21–23

    Google Scholar 

  • Laporte G (1992) The traveling salesman problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(2): 231–247

    Article  MATH  Google Scholar 

  • Leacock C, Chodorow M (1998) Combining local context and WordNet similarity for word sense identification. In: Fellbaum C (ed) WordNet: an electronic lexical database. MIT Press, Cambridge, pp 305–332

    Google Scholar 

  • Lee YK, Ng HT, Chia TK (2004) Supervised word sense disambiguation with support vector machines and multiple knowledge sources. In: Mihalcea R, Edmonds P (eds) Senseval-3: 3rd international workshop on the evaluation of systems for the semantic analysis of text. Association for Computational Linguistics, Barcelona, pp 137–140

    Google Scholar 

  • 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. SIGDOC ’86. ACM, New York, pp 24–26

  • Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of the 15th international conference on machine learning. ICML ’98. Morgan Kaufmann Publishers Inc, San Francisco, pp 296–304

  • Mihalcea R, Moldovan DI (2001) eXtended WordNet: progress report. In: Proceedings of NAACL workshop on WordNet and other lexical resources, pp 95–100

  • Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11): 39–41

    Article  Google Scholar 

  • Miller GA, Charles WG (1991) Contextual correlates of semantic similarity. Lang Cogn Process 6: 1–28

    Article  Google Scholar 

  • Miller GA et al (1993) A semantic concordance. In: Proceedings of the workshop on human language technology. HLT ’93. Association for Computational Linguistics, Stroudsburg, pp 303–308

  • Molina A, Pla F, Segarra E (2002) A hidden Markov model approach to word sense disambiguation. In: Proceedings of the 8th Ibero-American conference on AI: advances in artificial intelligence. IBERAMIA. Springer, London, pp 655–663

  • Navigli R (2009) Word sense disambiguation: a survey. ACM Comput Surv 41(2): 10–11069

    Article  Google Scholar 

  • Navigli R, Velardi P (2005) Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Trans Pattern Anal Mach Intell 27(7): 1075–1086

    Article  Google Scholar 

  • Navigli R, Litkowski KC, Hargraves O (2007) SemEval-2007 task 07: coarse-grained English all-words task. In: Proceedings of the 4th international workshop on semantic evaluations (SemEval-2007). Association for Computational Linguistics, Prague, pp 30–35

  • Page L et al (1999) The PageRank citation ranking: bringing order to the web. Stanford InfoLab

  • Pedersen T, Banerjee S, Patwardhan S (2005) Maximizing semantic relatedness to perform word sense disambiguation

  • Pedersen T, Bruce R (1997) Distinguishing word senses in untagged text. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP-97), Providence

  • 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. Association for Computational Linguistics, Uppsala, pp 1522–1531

  • Ponzetto SP, Strube M (2011) Taxonomy induction based on a collaboratively built knowledge repository. Artif Intell 175(9–10): 1737–1756

    Article  MathSciNet  Google Scholar 

  • Popescu M, Hristea F (2011) State of the art versus classical clustering for unsupervised word sense disambiguation. Artif Intell Rev 35(3): 241–264

    Article  Google Scholar 

  • Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th international joint conference on artificial intelligence—volume 1. Morgan Kaufmann Publishers Inc, San Francisco, pp 448–453

  • Ruiz-Casado M, Alfonseca E, Castells P (2005) Automatic assignment of wikipedia encyclopedic entries to WordNet Synsets. In: Advances in web intelligence, pp 380–386

  • Schwab D, Guillaume N (2011) A global ant colony algorithm for word sense disambiguation based on semantic relatedness. In: Pérez J et al (ed) Highlights in practical applications of agents and multiagent systems. Advances in intelligent and soft computing. Springer, Berlin, pp 257–264

  • Segond F et al (1997) An experiment in semantic tagging using hidden Markov model tagging. In: ACL/EACL workshop on automatic information extraction and building of lexical semantic resources for NLP applications, pp 78–81

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

  • Snyder B, Palmer M (2004) The English all-words task. In: Mihalcea R, Edmonds P (eds) Senseval-3: 3rd international workshop on the evaluation of systems for the semantic analysis of text. Association for Computational Linguistics, Barcelona, pp 41–43

  • Tran A et al (2010) TreeMatch: a fully unsupervised WSD system using dependency knowledge on a specific domain. In: Proceedings of the 5th international workshop on semantic evaluation. SemEval ’10. Association for Computational Linguistics, Stroudsburg, pp 396–401

  • Tsatsaronis G, Vazirgiannis M, Androutsopoulos I (2007) Word sense disambiguation with spreading activation networks generated from thesauri. In: Proceedings of the 20th international joint conference on artifical intelligence. Morgan Kaufmann Publishers Inc, San Francisco, pp 1725–1730

  • Vasilescu F, Langlais P, Lapalme G (2004) Evaluating variants of the lesk approach for disambiguating words. In: Proceedings of LREC 2004. Lisbonne, pp 633–636

  • Veronis J, Ide NM (1990) Word sense disambiguation with very large neural networks extracted from machine readable dictionaries. In: Proceedings of the 13th conference on computational linguistics—volume 2. COLING ’90. Association for Computational Linguistics, Morristown, pp 389–394

  • Wu Z, Palmer M (1994) Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on Association for Computational Linguistics. ACL ’94. Association for Computational Linguistics, Stroudsburg, pp 133–138

  • Yepes AJ, Aronson A (2010) Knowledge-based biomedical word sense disambiguation: comparison of approaches. BMC Bioinformatics 11(1): 569+

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zhang C, Zhou Y, Martin T (2008) Genetic word sense disambiguation algorithm. In: Proceedings of the 2008 2nd international symposium on intelligent information technology application—volume 01. IITA ’08. IEEE Computer Society, Washington, pp 123–127

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  1. School of Electrical Engineering, University of Ulsan, 93, Daehakro, Nam-gu, Ulsan, 680–749, Korea

    Kiem-Hieu Nguyen & Cheol-Young Ock

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  1. Kiem-Hieu Nguyen
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  2. Cheol-Young Ock
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Correspondence to Cheol-Young Ock.

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Nguyen, KH., Ock, CY. Word sense disambiguation as a traveling salesman problem. Artif Intell Rev 40, 405–427 (2013). https://doi.org/10.1007/s10462-011-9288-9

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