Word sense disambiguation as a traveling salesman problem

Artificial Intelligence Review

Volumn 40, Issue 4, 2013, Pages 405-427

  Nguyen, Kiem Hieu ^a   Ock, Cheol Young ^a  

^a University of Ulsan   (South Korea)

Author keywords

Ant colony optimization; Lesk algorithm; Semantic relatedness; Traveling salesman problem; Word sense disambiguation; WordNet

Indexed keywords

KNOWLEDGE-BASED METHODS; LEXICAL DISAMBIGUATION; NATURE-INSPIRED ALGORITHMS; SEMANTIC RELATEDNESS; UNSUPERVISED METHOD; VECTOR SPACE MODELS; WORD SENSE DISAMBIGUATION; WORDNET;

ALGORITHMS; ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; KNOWLEDGE BASED SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; REINFORCEMENT LEARNING; TRAVELING SALESMAN PROBLEM; VECTOR SPACES;

HEURISTIC METHODS;

EID: 84890425797     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-011-9288-9     Document Type: Article

References (51)

1. 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
2. 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
3. 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
4. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1-7): 107-117
5. Buckley C (1985) Implementation of the SMART information retrieval system. Cornell University, Ithaca
6. Budanitsky A, Hirst G (2006) Evaluating WordNet-based measures of lexical semantic relatedness. Comput Linguist 32(1): 13-47
7. 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
8. Cottrell GW (1989) A connectionist approach to word sense disambiguation. Morgan Kaufmann Publishers Inc, San Francisco
9. 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
10. 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
11. Dorigo M, Gambardella LM (1997) Ant colonies for the traveling salesman problem
12. 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
13. Gelbukh AF, Sidorov G, Han S-Y (2005) On some optimization heuristics for lesk-like WSD algorithms. In: NLDB, pp 402-405
14. 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
15. Hirst GJ (1984) Semantic interpretation against ambiguity. Brown University, Providence
16. 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
17. 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
18. Kleinberg JM (1999) Hubs, authorities, and communities. ACM Comput Surv 31(4es):21-23
19. Laporte G (1992) The traveling salesman problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(2): 231-247
20. 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
21. 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
22. 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
23. 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
24. Mihalcea R, Moldovan DI (2001) eXtended WordNet: progress report. In: Proceedings of NAACL workshop on WordNet and other lexical resources, pp 95-100
25. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11): 39-41
26. Miller GA, Charles WG (1991) Contextual correlates of semantic similarity. Lang Cogn Process 6: 1-28
27. 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
28. 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
29. Navigli R (2009) Word sense disambiguation: a survey. ACM Comput Surv 41(2): 10-11069
30. 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
31. 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
32. Page L et al (1999) The PageRank citation ranking: bringing order to the web. Stanford InfoLab
33. Pedersen T, Banerjee S, Patwardhan S (2005) Maximizing semantic relatedness to perform word sense disambiguation
34. 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
35. 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
36. Ponzetto SP, Strube M (2011) Taxonomy induction based on a collaboratively built knowledge repository. Artif Intell 175(9-10): 1737-1756
37. Popescu M, Hristea F (2011) State of the art versus classical clustering for unsupervised word sense disambiguation. Artif Intell Rev 35(3): 241-264
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. Yepes AJ, Aronson A (2010) Knowledge-based biomedical word sense disambiguation: comparison of approaches. BMC Bioinformatics 11(1): 569+
50. Yuret D, Yatbaz MA (2010) The noisy channel model for unsupervised word sense disambiguation. Comput Linguist 36(1): 111-127
51. 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