research-article

Word sense disambiguation: A survey

Author:

Roberto NavigliAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 41, Issue 2

Article No.: 10, Pages 1 - 69

https://doi.org/10.1145/1459352.1459355

Published: 23 February 2009 Publication History

Abstract

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

References

[1]

Abney, S. 2004. Understanding the Yarowsky algorithm. Computat. Ling. 30, 3, 365--395.

Digital Library

[2]

Abney, S. and Light, M. 1999. Hiding a semantic class hierarchy in a Markov model. In Proceedings of the ACL Workshop on Unsupervised Learning in Natural Language Processing (College Park, MD). 1--8.

[3]

Agirre, E., Ansa, O., Martinez, D., and Hovy, E. 2001. Enriching WordNet concepts with topic signatures. In Proceedings of the NAACL Workshop on WordNet and Other lexical Resources: Applications, Extensions and Customizations (Pittsburg, PA). 23--28.

[4]

Agirre, E. and Edmonds, P., Eds. 2006. Word Sense Disambiguation: Algorithms and Applications. Springer, New York, NY.

Digital Library

[5]

Agirre, E. and Lopez de Lacalle, O. 2003. Clustering WordNet word senses. In Proceedings of the Conference on Recent Advances on Natural Language Processing (RANLP, Borovets, Bulgaria). 121--130.

[6]

Agirre, E. and Lopez de Lacalle, O. 2007. UBC-ALM: Combining k-nn with SVD for WSD. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 342--345.

[7]

Agirre, E., Magnini, B., de Lacalle, O., Otegi, A., Rigau, G., and Vossen, P. 2007a. Semeval-2007 task 01: Evaluating WSD on cross-language information retrieval. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 1--6.

[8]

Agirre, E., Màrquez, L., and Wicentowski, R., Eds. 2007b. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval). Association for Computational Linguistics, Prague, Czech Republic.

[9]

Agirre, E. and Martinez, D. 2000. Exploring automatic word sense disambiguation with decision lists and the web. In Proceedings of the 18th International Conference on Computational Linguistics (COLING, Saarbrücken, Germany). 11--19.

[10]

Agirre, E. and Martinez, D. 2001. Learning class-to-class selectional preferences. In Proceedings of the 5th Conference on Computational Natural Language Learning (CoNLL, Toulouse, France). 15--22.

Digital Library

[11]

Agirre, E., Martínez, D., Lopez de Lacalle, O., and Soroa, A. 2006. Two graph-based algorithms for state-of-the-art wsd. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (Sydney, Australia). 585--593.

[12]

Agirre, E. and Rigau, G. 1996. Word sense disambiguation using conceptual density. In Proceedings of the 16th International Conference on Computational Linguistics (COLING, Copenhagen, Denmark). 16--22.

Digital Library

[13]

Agirre, E. and Soroa, A. 2007. Semeval-2007 task 02: Evaluating word sense induction and discrimination systems. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 7--12.

[14]

Agirre, E. and Stevenson, M. 2006. Knowledge sources for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 217--251.

[15]

Aleman-Meza, B., Nagarajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A., Arpinar, I., Joshi, A., and Finin, T. 2006. Semantic analytics on social networks: Experiences in addressing the problem of conflict of interest detection. In Proceedings of the 15th International Conference on World Wide Web (WWW, Edinburgh, Scotland, U.K.). 407--416.

Digital Library

[16]

Alpaydin, E. 2004. Introduction to Machine Learning. MIT Press, Cambridge, MA.

Digital Library

[17]

Amsler, R. A. 1980. The structure of the Merriam-Webster pocket dictionary. Ph.D. dissertation. University of Texas at Austin, Austin, TX.

Digital Library

[18]

Anaya-Sánchez, H., Pons-Porrata, A., and Berlanga-Llavori, R. 2007. TKB-UO: Using sense clustering for wsd. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 322--325.

[19]

Artiles, J., Gonzalo, J., and Sekine, S. 2007. The Semeval-2007 WEPS evaluation: Establishing a benchmark for the Web people search task. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 64--69.

[20]

Atkins, S. 1993. Tools for computer-aided corpus lexicography: The Hector project. Acta Linguistica Hungarica 41, 5--72.

[21]

Banerjee, S. and Pedersen, T. 2003. Extended gloss overlaps as a measure of semantic relatedness. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI, Acapulco, Mexico). 805--810.

[22]

Bar-Hillel, Y. 1960. The present status of automatic translation of languages. Advan. Comput. 1, 91--163.

[23]

Barzilay, R. and Elhadad, M. 1997. Using lexical chains for text summarization. In Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization (Madrid, Spain). 10--17.

[24]

Basili, R., Rocca, M. D., and Pazienza, M. T. 1997. Contextual word sense tuning and disambiguation. Appl. Artific. Intell. 11, 3, 235--262.

[25]

Bentivogli, L. and Pianta, E. 2005. Exploiting parallel texts in the creation of multilingual semantically annotated resources: The MultiSemCor Corpus. J. Nat. Lang. Eng. 11, 3, 247--261.

Digital Library

[26]

Berendt, B. and Navigli, R. 2006. Finding your way through blogspace: Using semantics for cross-domain blog analysis. In Proceedings of the AAAI Spring Symposium 2006 (AAAIS) on Computational Approaches to Analysing Weblogs (CAAW, Palo Alto, CA). 1--8.

[27]

Bernard, J. R. L., Ed. 1986. Macquarie Thesaurus. Macquarie, Sydney, Australia.

[28]

Berners-Lee, T., Hendler, J., and Lassila. O. 2001. The semantic Web. http://www.sciam.com/article.cfm?id=the-semantic-web&page=2.

[29]

Black, E. 1988. An experiment in computational discrimination of English word senses. IBM J. Res. Devel. 32, 2, 185--194.

Digital Library

[30]

Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022.

[31]

Bordag, S. 2006. Word sense induction: Triplet-based clustering and automatic evaluation. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL, Trento, Italy). 137--144.

[32]

Boser, B. E., Guyon, I. M., and Vapnik, V. N. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory (Pittsburgh, PA). 144--152.

Digital Library

[33]

Boyd-Graber, J., Fellbaum, C., Osherson, D., and Schapire, R. 2006. Adding dense, weighted connections to WordNet. In Proceedings of the 3rd International WordNet Conference (Jeju Island, Korea).

[34]

Brants, T. and Franz, A. 2006. Web 1t 5-gram, ver. 1, ldc2006t13. Linguistic Data Consortium, Philadelphia, PA.

[35]

Brin, S. and Page, M. 1998. Anatomy of a large-scale hypertextual Web search engine. In Proceedings of the 7th Conference on World Wide Web (Brisbane, Australia). 107--117.

Digital Library

[36]

Brody, S., Navigli, R., and Lapata, M. 2006. Ensemble methods for unsupervised WSD. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics (COLING-ACL, Sydney, Australia). 97--104.

Digital Library

[37]

Brown, P. F., Pietra, S. A. D., Pietra, V. J. D., and Mercer, R. L. 1991. Word-sense disambiguation using statistical methods. In Proceedings of 29th Annual Meeting of the Association for Computational Linguistics (Berkeley, CA). 264--270.

Digital Library

[38]

Bruce, R. and Wiebe, J. 1994. Word-sense disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL, Las Cruces, NM). 139--145.

Digital Library

[39]

Bruce, R. and Wiebe, J. 1999. Decomposable modeling in natural language processing. Comput. Ling. 25, 2, 195--207.

Digital Library

[40]

Budanitsky, A. and Hirst, G. 2006. Evaluating WordNet-based measures of semantic distance. Computat. Ling. 32, 1, 13--47.

Digital Library

[41]

Buitelaar, P. 1998. Corelex: An ontology of systematic polysemous classes. In Formal Ontology in Information Systems, N. Guarino, Ed. IOS Press, Amsterdam, The Netherlands. 221--235.

[42]

Buitelaar, P., Magnini, B., Strapparava, C., and Vossen, P. 2006. Domain-specific WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 275--298.

[43]

Buitelaar, P. and Sacaleanu, B. 2001. Ranking and selecting synsets by domain relevance. In Proceedings of the NAACL Workshop on WordNet and Other lexical Resources: Applications, Extensions and Customizations (Pittsburgh, PA).

[44]

Bunke, H. and Sanfeliu, A., Eds. 1990. Syntactic and Structural Pattern Recognition: Theory and Applications. Vol. 7. World Scientific Series in Computer Science, World Scientific, Singapore

[45]

Cai, J. F., Lee, W. S., and Teh, Y. W. 2007. NUS-ML: Improving word sense disambiguation using topic features. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 249--252.

[46]

Cardie, C. and Mooney, R. J. 1999. Guest editors' introduction: Machine learning and natural language. Mach. Learn. 34, 1--3, 5--9.

Digital Library

[47]

Carpuat, M. and Wu, D. 2005. Word sense disambiguation vs. statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL, Ann Arbor, MI). 387--394.

Digital Library

[48]

Carpuat, M. and Wu, D. 2007. Improving statistical machine translation using word sense disambiguation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL, Prague, Czech Republic). 61--72.

[49]

Chan, Y. S. and Ng, H. T. 2005. Scaling up word sense disambiguation via parallel texts. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI, Pittsburgh, PA). 1037--1042.

[50]

Chan, Y. S., Ng, H. T., and Chiang, D. 2007a. Word sense disambiguation improves statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (Prague, Czech Republic). 33--40.

[51]

Chan, Y. S., Ng, H. T., and Zhong, Z. 2007b. 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, Prague, Czech Republic). 253--256.

[52]

Charniak, E., Blaheta, D., Ge, N., Hall, K., Hale, J., and Johnson, M. 2000. Bllip 1987-89 WSJ corpus release 1. Tech. rep. LDC2000T43. Linguistic Data Consortium (Philadelphia, PA).

[53]

Chklovski, T. and Mihalcea, R. 2002. Building a sense tagged corpus with Open Mind Word Expert. In Proceedings of ACL 2002 Workshop on WSD: Recent Successes and Future Directions (Philadelphia, PA).

Digital Library

[54]

Chklovski, T. and Mihalcea, R. 2003. Exploiting agreement and disagreement of human annotators for word sense disambiguation. In Proceedings of the Conference on Recent Advances on Natural Language Processing (RANLP, Borovets, Bulgaria).

[55]

Chklovski, T., Mihalcea, R., Pedersen, T., and Purandare, A. 2004. The Senseval-3 multilingual English-Hindi lexical sample task. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 5--8.

[56]

Chklovski, T. and Pantel, P. 2004. Verbocean: Mining the Web for fine-grained semantic verb relations. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP, Barcelona, Spain).

[57]

Chodorow, M., Byrd, R., and Heidorn, G. 1985. Extracting semantic hierarchies from a large on-line dictionary. In Proceedings of the 23rd Annual Meeting of the Association for Computational Linguistics (Chicago, IL). 299--304.

Digital Library

[58]

Church, K. W. and Rau, L. F. 1995. Commercial applications of natural language processing. Commun. ACM 38, 11, 71--79.

Digital Library

[59]

Ciaramita, M. and Altun, Y. 2006. Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP, Sydney, Australia). 594--602.

[60]

Ciaramita, M. and Johnson, M. 2000. Explaining away ambiguity: Learning verb selectional restrictions with Bayesian networks. In Proceedings of the 18th International Conference on Computational Linguistics (COLING, Saarbrücken, Germany). 187--193.

Digital Library

[61]

Cimiano, P. 2006. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, New York, NY.

Digital Library

[62]

Clark, S. and Weir, D. 2002. Class-based probability estimation using a semantic hierarchy. Computat. Ling. 28, 2, 187--206.

Digital Library

[63]

Clear, J. 1993. The British National Corpus. In The Digital Word: Text-Based Computing in the Humanities, P. Delany and G. P. Landow, Eds. MIT Press, Cambridge, MA. 163--187.

Digital Library

[64]

Cohn, D., Atlas, R., and Ladner, R. 1994. Improving generalization with active learning. Mach. Learn. 15, 2, 201--221.

Digital Library

[65]

Cohn, T. 2003. Performance metrics for word sense disambiguation. In Proceedings of the Australasian Language Technology Workshop (Melbourne, Australia). 49--56.

[66]

Collins, M. 2004. Parameter estimation for statistical parsing models: Theory and practice of distribution-free methods. In New Developments in Parsing Technology, H. Bunt, J. Carroll, and G. Satta, Eds. Kluwer, Dordrecht, The Netherlands, 19--55.

Digital Library

[67]

Cost, S. and Salzberg, S. 1993. A weighted nearest neighbor algorithm for learning with symbolic features. Mach. Learn. 10, 1, 57--78.

Digital Library

[68]

Cottrell, G. W. 1989. A Connectionist Approach to Word Sense Disambiguation. Pitman, London, U.K.

Digital Library

[69]

Crestan, E., El-Bze, M., and Loupy, C. D. 2001. Improving WSD with multi-level view of context monitored by similarity measure. In Proceedings of the 2nd International Workshop on Evaluating Word Sense Disambiguation Systems (Senseval-2, Toulouse, France). 67--70.

[70]

Cruse, D. A. 1986. Lexical Semantics. Cambridge University Press, Cambridge, U.K.

[71]

Cuadros, M. and Rigau, G. 2006. Quality assessment of large scale knowledge resources. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP, Sydney, Australia). 534--541.

[72]

Cucchiarelli, A. and Velardi, P. 1998. Finding a domain-appropriate sense inventory for semantically tagging a corpus. J. Nat. Lang. Eng. 4, 4, 325--344.

Digital Library

[73]

Daelemans, W., van den Bosch, A., and Zavrel, J. 1999. Forgetting exceptions is harmful in language learning. Mach. Learn. 34, 1, 11--41.

Digital Library

[74]

Dagan, I. and Engelson, S. 1995. Selective sampling in natural language learning. In Proceedings of the IJCAI Workshop on New Approaches to Learning for Natural Language Processing (Montreal, P.Q., Canada). 41--48.

[75]

Dagan, I. and Itai, A. 1994. Word sense disambiguation using a second language monolingual corpus. Computat. Ling. 20, 4, 563--596.

Digital Library

[76]

Decadt, B., Hoste, V., Daelemans, W., and van den Bosch, A. 2004. GAMBL, genetic algorithm optimization of memory-based WSD. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 108--112.

[77]

Dempster, A., Laird, N., and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. 39, 1--38.

[78]

Diab, M. 2003. Word sense disambiguation within a multilingual framework. Ph.D. dissertation. University of Maryland, College Park, College of Park, MD.

Digital Library

[79]

Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomline, J. A., and Zien, J. Y. 2003. Semtag and Seeker: Bootstrapping the semantic Web via automated semantic annotation. In Proceedings of the 20th International Conference on World Wide Web (WWW, Budapest, Hungary). 178--186.

Digital Library

[80]

Dolan, W. B. 1994. Word sense ambiguation: Clustering related senses. In Proceedings of the 15th Conference on Computational Linguistics (COLING). Kyoto, Japan.

Digital Library

[81]

Dorow, B. and Widdows, D. 2003. Discovering corpus-specific word senses. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics (Budapest, Hungary). 79--82.

Digital Library

[82]

Duffield, C. J., Hwang, J. D., Brown, S. W., Dligach, D., Vieweg, S. E., Davis, J., and Palmer, M. 2007. Criteria for the manual grouping of verb senses. In Proceedings of the Linguistic Annotation Workshop (Prague, Czech Republic).

[83]

Edmonds, P. 2000. Designing a task for SENSEVAL-2. Tech. note. University of Brighton, Brighton. U.K.

[84]

Edmonds, P. and Cotton, S. 2001. Senseval-2: Overview. In Proceedings of the 2nd International Workshop on Evaluating Word Sense Disambiguation Systems (Senseval-2, Toulouse, France). 1--6.

[85]

Edmonds, P. and Kilgarriff, A. 2002. Introduction to the special issue on evaluating word sense disambiguation systems. J. Nat. Lang. Eng. 8, 4, 279--291.

Digital Library

[86]

Escudero, G., Màrquez, L., and Rigau, G. 2000a. Boosting applied to word sense disambiguation. In Proceedings of the 11th European Conference on Machine Learning (ECML, Barcelona, Spain). 129--141.

Digital Library

[87]

Escudero, G., Màrquez, L., and Rigau, G. 2000b. Naive Bayes and exemplar-based approaches to word sense disambiguation revisited. In Proceedings of the 14th European Conference on Artificial Intelligence (ECAI, Berlin, Germany). 421--425.

[88]

Escudero, G., Màrquez, L., and Rigau, G. 2000c. On the portability and tuning of supervised word sense disambiguation. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC, Hong Kong, China). 172--180.

Digital Library

[89]

Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., and Yates, A. 2004. Web-scale information extraction in Knowitall. In Proceedings of the 13th International Conference on World Wide Web (WWW, New York, NY). 100--110.

Digital Library

[90]

Fellbaum, C., Ed. 1998. WordNet: An Electronic Database. MIT Press, Cambridge, MA.

[91]

Fellbaum, C., Palmer, M., Dang, H. T., Delfs, L., and Wolf, S. 2001. Manual and automatic semantic annotation with WordNet. In Proceedings of the NAACL Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations (Pittsburgh, PA). 3--10.

[92]

Florian, R., Cucerzan, S., Schafer, C., and Yarowsky, D. 2002. Combining classifiers for word sense disambiguation. J. Nat. Lang. Eng. 8, 4, 1--14.

Digital Library

[93]

Freund, Y. and Schapire, R. 1999. A short introduction to boosting. J. Japanese Soci. Artific. Intell. 14, 771--780.

[94]

Fu, K. 1982. Syntactic Pattern Recognition and Applications. Prentice-Hall, Engelwood Cliffs, NJ.

[95]

Fujii, A., Inui, K., Tokunaga, T., and Tanaka, H. 1998. Selective sampling for example-based word sense disambiguation. Computat. Ling. 24, 4, 573--598.

Digital Library

[96]

Gale, W. A., Church, K., and Yarowsky, D. 1992a. Estimating upper and lower bounds on the performance of word-sense disambiguation programs. In Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics (Newark, NJ). 249--256.

Digital Library

[97]

Gale, W. A., Church, K., and Yarowsky, D. 1992b. A method for disambiguating word senses in a corpus. Comput. Human. 26, 415--439.

[98]

Gale, W. A., Church, K., and Yarowsky, D. 1992c. One sense per discourse. In Proceedings of the DARPA Speech and Natural Language Workshop (Harriman, NY). 233--237.

Digital Library

[99]

Gale, W. A., Church, K., and Yarowsky, D. 1992d. Using bilingual materials to develop word sense disambiguation methods. In Proceedings of the 4th International Conference on Theoretical and Methodological Issues in Machine Translation (Montreal, P.Q., Canada). 101--112.

[100]

Galley, M. and McKeown, K. 2003. Improving word sense disambiguation in lexical chaining. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI, Acapulco, Mexico). 1486--1488.

Digital Library

[101]

Girju, R., Badulescu, A., and Moldovan, D. 2003. Learning semantic constraints for the automatic discovery of part-whole relations. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (Edmonton, Alta., Canada). 1--8.

Digital Library

[102]

Gliozzo, A., Magnini, B., and Strapparava, C. 2004. Unsupervised domain relevance estimation for word sense disambiguation. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP, Barcelona, Spain). 380--387.

[103]

Golub, G. H. and van Loan, C. F. 1989. Matrix Computations. The John Hopkins University Press, Baltimore, MD.

[104]

Graff, D. 2003. English gigaword. Tech. rep. LDC2003T05. Linguistic Data Consortium, Philadelphia, PA.

[105]

Grozea, C. 2004. Finding optimal parameter settings for high performance word sense disambiguation. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 125--128.

[106]

Gruber, T. R. 1993. Toward principles for the design of ontologies used for knowledge sharing. In Proceedings of the International Workshop on Formal Ontology (Padova, Italy).

[107]

Halliday, M. A. and Hasan, R., Eds. 1976. Cohesion in English. Longman Group Ltd, London, U.K.

[108]

Harabagiu, S., Miller, G., and Moldovan, D. 1999. WordNet 2—a morphologically and semantically enhanced resource. In Proceedings of the ACL SIGLEX Workshop: Standardizing Lexical Resources. 1--8.

[109]

Hassan, H., Hassan, A., and Noeman, S. 2006. Graph based semi-supervised approach for information extraction. In Proceedings of the TextGraphs Workshop in the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL, New York, NY). 9--16.

[110]

Hawkins, P. and Nettleton, D. 2000. Large scale WSD using learning applied to senseval. Comput. Human. 34, 1-2, 135--140.

[111]

Hearst, M. 1991. Noun homograph disambiguation using local context in large text corpora. In Proceedings of the 7th Annual Conference of the UW Centre for the New OED and Text Research: Using Corpora (Oxford, U.K). 1--19.

[112]

Hearst, M. 1992. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14^th International Conference on Computational Linguistics (COLING, Nantes, France). 539--545.

Digital Library

[113]

Hindle, D. and Rooth, M. 1993. Structural ambiguity and lexical relations. Computat. Ling. 19, 1, 103--120.

Digital Library

[114]

Hirst, G., Ed. 1987. Semantic Interpretation and the Resolution of Ambiguity. Cambridge University Press, Cambridge, U.K.

Digital Library

[115]

Hirst, G. and St-Onge, D. 1998. Lexical chains as representations of context for the detection and correction of malapropisms. In WordNet: An Electronic Lexical Database, C. Fellbaum, Ed. MIT Press, Cambridge, MA, 305--332.

[116]

Hoste, V., Hendrickx, I., Daelemans, W., and van den Bosch, A. 2002. Parameter optimization for machine learning of word sense disambiguation. J. Nat. Lang. Eng. 8, 4, 311--325.

Digital Library

[117]

Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., and Weischedel, R. 2006. Ontonotes: The 90&percnt; solution. In Proceedings of the Human Language Technology Conference of the NAACL, Comp. Volume (New York, NY). 57--60.

[118]

Ide, N. 2000. Cross-lingual sense determination: Can it work&quest; Comput. Human. 34, 1--2, 223--234.

[119]

Ide, N. 2006. Making senses: Bootstrapping sense-tagged lists of semantically-related words. In Computational Linguistics and Intelligent Text, A. Gelbukh, Ed. Lecture Notes in Computer Science, vol. 3878. Springer, Berlin, Germeny, 13--27.

[120]

Ide, N., Erjavec, T., and Tufis, D. 2001. Automatic sense tagging using parallel corpora. In Proceedings of the 6th Natural Language Processing Pacific Rim Symposium (Tokyo, Japan). 83--89.

[121]

Ide, N., Erjavec, T., and Tufis, D. 2002. Sense discrimination with parallel corpora. In Proceedings of ACL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions (Philadelphia, PA). 54--60.

Digital Library

[122]

Ide, N. and Suderman, K. 2006. Integrating linguistic resources: The American National Corpus model. In Proceedings of the 5th Language Resources and Evaluation Conference (LREC, Genoa, Italy).

[123]

Ide, N. and Véronis, J. 1993. Extracting knowledge bases from machine-readable dictionaries: Have we wasted our time&quest; In Proceedings of the Workshop on Knowledge Bases and Knowledge Structures (Tokyo, Japan). 257--266.

[124]

Ide, N. and Véronis, J. 1998. Word sense disambiguation: The state of the art. Computat. Ling. 24, 1, 1--40.

Digital Library

[125]

Ide, N. and Wilks, Y. 2006. Making sense about sense. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 47--73.

[126]

Jacquemin, B., Brun, C., and Roux, C. 2002. Enriching a text by semantic disambiguation for information extraction. In Proceedings of the Workshop on Using Semantics for Information Retrieval and Filtering in the 3rd International Conference on Language Resources and Evaluations (LREC, Las Palmas, Spain).

[127]

Jiang, J. J. and Conrath, D. W. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the 10th International Conference on Research in Computational Linguistics (Taiwan, ROC).

[128]

Joachims, T. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (ECML, Heidelberg, Germany). 137--142.

Digital Library

[129]

Johnston, M. and Busa, F. 1996. Qualia structure and the compositional interpretation of compounds. In Proceedings of the ACL SIGLEX Workshop on Breadth and Depth of Semantic Lexicons (Santa Cruz, CA).

[130]

Jurafsky, D. and Martin, J. 2000. Speech and Language Processing. Prentice Hall, Upper Saddle River, NJ.

Digital Library

[131]

Kelly, E. and Stone, P. 1975. Computer Recognition of English Word Senses. Vol. 3 of North Holland Linguistics Series. Elsevier, Amsterdam, The Netherlands.

[132]

Keok, L. Y. and Ng, H. T. 2002. An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP, Philadelphia, PA). 41--48.

Digital Library

[133]

Kilgarriff, A. 1997. I don't believe in word senses. Comput. Human. 31, 2, 91--113.

[134]

Kilgarriff, A. 1998. Senseval: An exercise in evaluating word sense disambiguation programs. In Proceedings of the 1st International Conference on Language Resources and Evaluation (LREC, Granada, Spain). 1255--1258.

[135]

Kilgarriff, A. 2006. Word senses. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 29--46.

[136]

Kilgarriff, A. and Grefenstette, G. 2003. Introduction to the special issue on the Web as corpus. Computat. Ling. 29, 3, 333--347.

Digital Library

[137]

Kilgarriff, A. and Palmer, M. 2000. Introduction to the special issue on Senseval. Comput. Human. 34, 1-2, 1--13.

[138]

Kilgarriff, A. and Rosenzweig, J. 2000. English Senseval: Report and results. In Proceedings of the 2nd Conference on Language Resources and Evaluation (LREC, Athens, Greece).

[139]

Kilgarriff, A., Rychly, P., Smrz, P., and Tugwell, D. 2004. The Sketch Engine. In Proceedings of the 11th EURALEX International Congress (Lorient, France). 105--116.

[140]

Kilgarriff, A. and Yallop, C. 2000. What's in a thesaurus&quest; In Proceedings of the 2nd Conference on Language Resources and Evaluation (LREC, Athens, Greece). 1371--1379.

[141]

Klein, D., Toutanova, K., Ilhan, T. H., Kamvar, S. D., and Manning, C. D. 2002. Combining heterogeneous classifiers for word-sense disambiguation. In Proceedings of the ACL workshop on Word Sense Disambiguation: Recent Successes and Future Directions (Philadelphia, PA). 74--80.

Digital Library

[142]

Kohomban, U. S. and Lee, W. S. 2005. Learning semantic classes for word sense disambiguation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (Ann Arbor, MI). 34--41.

Digital Library

[143]

Krovetz, R. and Croft, W. B. 1992. Lexical ambiguity and information retrieval. ACM Trans. Inform. Syst. 10, 2, 115--141.

Digital Library

[144]

Kucera, H. and Francis, W. N. 1967. Computational Analysis of Present-Day American English. Brown University Press, Providence, RI.

[145]

Kudo, T. and Matsumoto, Y. 2001. Chunking with support vector machines. In Proceedings of NAACL (Pittsburgh, PA). 137--142.

Digital Library

[146]

Lapata, M. and Keller, F. 2007. An information retrieval approach to sense ranking. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL, Rochester, NY). 348--355.

[147]

Leacock, C. and Chodorow, M. 1998. Combining local context and WordNet similarity for word sense identification. In WordNet: An electronic Lexical Database, C. Fellbaum, Ed. MIT Press, Cambridge, MA, 265--283.

[148]

Leacock, C., Chodorow, M., and Miller, G. 1998. Using corpus statistics and WordNet relations for sense identification. Computat. Ling. 24, 1, 147--166.

Digital Library

[149]

Leacock, C., Towell, G., and Voorhees, E. 1993. Corpus-based statistical sense resolution. In Proceedings of the ARPA Workshop on Human Language Technology (Princeton, NJ). 260--265.

Digital Library

[150]

Lee, L. 1999. Measures of distributional similarity. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (College Park, MD). 25--32.

Digital Library

[151]

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 SIGDOC (New York, NY). 24--26.

Digital Library

[152]

Li, H. and Abe, N. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computat. Ling. 24, 2, 217--244.

Digital Library

[153]

Lin, D. 1998a. Automatic retrieval and clustering of similar words. In Proceedings of the 17th International Conference on Computational linguistics (COLING, Montreal, P.Q., Canada). 768--774.

Digital Library

[154]

Lin, D. 1998b. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning (ICML, Madison, WI). 296--304.

Digital Library

[155]

Lin, D. and Pantel, P. 2002. Discovering word senses from text. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Edmonton, Alta., Canada). 613--619.

Digital Library

[156]

Litkowski, K. C. 1978. Models of the semantic structure of dictionaries. Amer. J. Computat. Ling. 81, 25--74.

[157]

Litkowski, K. C. 2005. Computational lexicons and dictionaries. In Encyclopedia of Language and Linguistics (2nd ed.), K. R. Brown, Ed. Elsevier Publishers, Oxford, U.K., 753--761.

[158]

Litkowski, K. C. and Hargraves, O. 2007. Semeval-2007 task 06: Word-sense disambiguation of prepositions. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 24--29.

[159]

Luger, G. F. 2004. Artificial Intelligence: Structures and Strategies for Complex Problem-Solving, 5th ed. Addison Wesley, Reading, MA.

Digital Library

[160]

Magnini, B. and Cavaglià, G. 2000. Integrating subject field codes into WordNet. In Proceedings of the 2nd Conference on Language Resources and Evaluation (LREC, Athens, Greece). 1413--1418.

[161]

Malin, B., Airoldi, E., and Carley, K. M. 2005. A network analysis model for disambiguation of names in lists. Computat. Math. Organizat. Theo. 11, 2, 119--139.

Digital Library

[162]

Mallery, J. C. 1988. Thinking about foreign policy: Finding an appropriate role for artificial intelligence computers. Ph.D. dissertation. MIT Political Science Department, Cambridge, MA.

[163]

Manning, C. and Schütze, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA.

Digital Library

[164]

Markert, K. and Nissim, M. 2007. Semeval-2007 task 08: Metonymy resolution at Semeval-2007. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 36--41.

[165]

Màrquez, L., Escudero, G., Martìnez, D., and Rigau, G. 2006. Supervised corpus-based methods for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 167--216.

[166]

Martinez, D. 2004. Supervised word sense disambiguation: Facing current challenges, Ph.D. dissertation. University of the Basque Country, Spain.

[167]

McCarthy, D. 2006. Relating wordnet senses for word sense disambiguation. In Proceedings of the ACL Workshop on Making Sense of Sense (Trento, Italy). 17--24.

[168]

McCarthy, D. and Carroll, J. 2003. Disambiguating nouns, verbs and adjectives using automatically acquired selectional preferences. Computat. Ling. 29, 4, 639--654.

Digital Library

[169]

McCarthy, D., Koeling, R., Weeds, J., and Carroll, J. 2004. Finding predominant senses in untagged text. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (Barcelona, Spain). 280--287.

Digital Library

[170]

McCarthy, D., Koeling, R., Weeds, J., and Carroll, J. 2007. Unsupervised acquisition of predominant word senses. Computat. Ling. 33, 4, 553--590.

Digital Library

[171]

McCarthy, D. and Navigli, R. 2007. Semeval-2007 task 10: English lexical substitution task. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 48--53.

[172]

McCray, A. T. and Nelson, S. J. 1995. The representation of meaning in the UMLS. Meth. Inform. Med. 34, 193--201.

[173]

McCulloch, W. and Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115--133.

[174]

Mihalcea, R. 2002a. Bootstrapping large sense tagged corpora. In Proceedings of the 3rd International Conference on Language Resources and Evaluations (LREC, Las Palmas, Spain).

[175]

Mihalcea, R. 2002b. Word sense disambiguation with pattern learning and automatic feature selection. J. Nat. Lang. Eng. 8, 4, 348--358.

Digital Library

[176]

Mihalcea, R. 2004. Co-training and self-training for word sense disambiguation. In Proceedings of the 8th Conference on Computational Natural Language Learning (CoNLL, Boston, MA). 33--40.

[177]

Mihalcea, R. 2006. Knowledge-based methods for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, 107--131.

[178]

Mihalcea, R. and Edmonds, P., Eds. 2004. Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain).

[179]

Mihalcea, R. and Faruque, E. 2004. Senselearner: Minimally supervised word sense disambiguation for all words in open text. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 155--158.

[180]

Mihalcea, R. and Moldovan, D. 1999. An automatic method for generating sense tagged corpora. In Proceedings of the 16th National Conference on Artificial Intelligence (AAAI, Orlando, FL). 461--466.

Digital Library

[181]

Mihalcea, R., Tarau, P., and Figa, E. 2004. Pagerank on semantic networks, with application to word sense disambiguation. In Proceedings of the 20th International Conference on Computational Linguistics (COLING, Geneva, Switzerland). 1126--1132.

Digital Library

[182]

Miller, G. A., Beckwith, R., Fellbaum, C. D., Gross, D., and Miller, K. 1990. WordNet: An online lexical database. Int. J. Lexicograph. 3, 4, 235--244.

[183]

Miller, G. A., Leacock, C., Tengi, R., and Bunker, R. T. 1993. A semantic concordance. In Proceedings of the ARPA Workshop on Human Language Technology. 303--308.

Digital Library

[184]

Mitchell, T. 1997. Machine Learning. McGraw Hill, New York, NY.

Digital Library

[185]

Mohammad, S. and Hirst, G. 2006. Determining word sense dominance using a thesaurus. In Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics (EACL, Trento, Italy). 121--128.

[186]

Mooney, R. J. 1996. Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. In Proceedings of the 1996 Conference on Empirical Methods in Natural Language Processing (EMNLP). 82--91.

[187]

Morris, J. and Hirst, G. 1991. Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computat. Ling. 17, 1.

Digital Library

[188]

Murata, M., Utiyama, M., Uchimoto, K., Ma, Q., and Isahara, H. 2001. Japanese word sense disambiguation using the simple Bayes and support vector machine methods. In Proceedings of the 2nd International Workshop on Evaluating Word Sense Disambiguation Systems (Senseval-2, Toulouse, France). 135--138.

[189]

Navigli, R. 2005. Semi-automatic extension of large-scale linguistic knowledge bases. In Proceedings of the 18th Florida Artificial Intelligence Research Society Conference (FLAIRS, Clearwater Beach, FL). 548--553.

[190]

Navigli, R. 2006a. Consistent validation of manual and automatic sense annotations with the aid of semantic graphs. Computat. Ling. 32, 2, 273--281.

Digital Library

[191]

Navigli, R. 2006b. Experiments on the validation of sense annotations assisted by lexical chains. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL, Trento, Italy). 129--136.

[192]

Navigli, R. 2006c. Meaningful clustering of senses helps boost word sense disambiguation performance. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics (COLING-ACL, Sydney, Australia). 105--112.

Digital Library

[193]

Navigli, R. 2008. A structural approach to the automatic adjudication of word sense disagreements. J. Nat. Lang. Eng. 14, 4, 547--573.

Digital Library

[194]

Navigli, R. and Lapata, M. 2007. Graph connectivity measures for unsupervised word sense disambiguation. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI, Hyderabad, India). 1683--1688.

[195]

Navigli, R., Litkowski, K. C., and Hargraves, O. 2007. Semeval-2007 task 07: Coarse-grained English all-words task. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 30--35.

[196]

Navigli, R. and Velardi, P. 2004. Learning domain ontologies from document warehouses and dedicated Websites. Computat. Ling. 30, 2, 151--179.

Digital Library

[197]

Navigli, R. and Velardi, P. 2005. Structural semantic interconnections: A knowledge-based approach to word sense disambiguation. IEEE Trans. Patt. Anal. Mach. Intell. 27, 7, 1075--1088.

Digital Library

[198]

Navigli, R., Velardi, P., and Gangemi, A. 2003. Ontology learning and its application to automated terminology translation. IEEE Intell. Syst. 18, 1, 22--31.

Digital Library

[199]

Ng, H. T. and Lee, H. B. 1996. Integrating multiple knowledge sources to disambiguate word senses: An examplar-based approach. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics (Santa Cruz, CA). 40--47.

Digital Library

[200]

Ng, H. T., Wang, B., and Chan, Y. S. 2003. Exploiting parallel texts for word sense disambiguation: an empirical study. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (Sapporo, Japan). 455--462.

Digital Library

[201]

Ng, T. H. 1997. Getting serious about word sense disambiguation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How&quest; (Washington D.C.). 1--7.

[202]

Ng, V. and Cardie, C. 2003. Weakly supervised natural language learning without redundant views. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL, Edmonton, Alta., Canada). 173--180.

Digital Library

[203]

Niu, C., Li, W., Srihari, R., and Li, H. 2005. Word independent context pair classification model for word sense disambiguation. In Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL, Ann Arbor, MI).

[204]

Niu, Z.-Y., Ji, D.-H., and Tan, C.-L. 2007. I2r: Three systems for word sense discrimination, Chinese word sense disambiguation, and English word sense disambiguation. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 177--182.

[205]

Palmer, M., Babko-Malaya, O., and Dang, H. T. 2004. Different sense granularities for different applications. In Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT/NAACL (Boston, MA). 49--56.

[206]

Palmer, M., Dang, H., and Fellbaum, C. 2007. Making fine-grained and coarse-grained sense distinctions, both manually and automatically. J. Nat. Lang. Eng. 13, 2, 137--163.

[207]

Palmer, M., Ng, H. T., and Dang, H. T. 2006. Evaluation of WSD systems. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 75--106.

[208]

Pantel, P. 2005. Inducing ontological co-occurrence vectors. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (Ann Arbor, MI). 125--132.

Digital Library

[209]

Pease, A., Niles, I., and Li, J. 2002. The suggested upper merged ontology: A large ontology for the semantic Web and its applications. In Proceedings of the AAAI-2002 Workshop on Ontologies and the Semantic Web (Edmonton, Alta., Canada).

[210]

Pedersen, T. 1998. Learning probabilistic models of word sense disambiguation, Ph.D. dissertation. Southern Methodist University, Dallas, TX.

[211]

Pedersen, T. 2006. Unsupervised corpus-based methods for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 133--166.

[212]

Pedersen, T., Banerjee, S., and Patwardhan, S. 2005. Maximizing semantic relatedness to perform word sense disambiguation. Res. rep. UMSI 2005/25. University of Minnesota Supercomputing Institute, Minneapolis, MN.

[213]

Pedersen, T. and Bruce, R. 1997. Distinguishing word senses in untagged text. In Proceedings of the 1997 Conference on Empirical Methods in Natural Language Processing (EMNLP, Providence, RI). 197--207.

[214]

Pedersen, T., Patwardhan, S., and Michelizzi, J. 2004. WordNet::Similarity—measuring the relatedness of concepts. In Proceedings of the 19th National Conference on Artificial Intelligence (AAAI, San Jose, CA) 144--152.

[215]

Pennacchiotti, M. and Pantel, P. 2006. Ontologizing semantic relations. In Proceedings of the 44th Association for Computational Linguistics (ACL) Conference joint with the 21th Conference on Computational Linguistics (COLING, Sydney, Australia). 793--800.

Digital Library

[216]

Philpot, A., Hovy, E., and Pantel, P. 2005. The Omega Ontology. In Proceedings of the IJCNLP Workshop on Ontologies and Lexical Resources (OntoLex, Jeju Island, South Korea). 59--66.

[217]

Pianta, E., Bentivogli, L., and Girardi, C. 2002. MultiWordNet: Developing an aligned multilingual database. In Proceedings of the 1st International Conference on Global WordNet (Mysore, India) 21--25.

[218]

Pradhan, S., Loper, E., Dligach, D., and Palmer, M. 2007. Semeval-2007 task-17: English lexical sample, SRL and all words. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 87--92.

[219]

Proctor, P., Ed. 1978. Longman Dictionary of Contemporary English. Longman Group, Harlow, U.K.

[220]

Purandare, A. and Pedersen, T. 2004. Improving word sense discrimination with gloss augmented feature vectors. In Proceedings of the Workshop on Lexical Resources for the Web and Word Sense Disambiguation (Puebla, Mexico). 123--130.

[221]

Pustejovsky, J. 1991. The generative lexicon. Computat. Ling. 17, 4, 409--441.

Digital Library

[222]

Pustejovsky, J. 1995. The Generative Lexicon. MIT Press, Cambridge, MA.

[223]

Quinlan, J. R. 1986. Induction of decision trees. Mach. Learn. 1, 1, 81--106.

[224]

Quinlan, J. R. 1993. Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA.

Digital Library

[225]

Rada, R., Mili, H., Bicknell, E., and Blettner, M. 1989. Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybernet. 19, 1, 17--30.

[226]

Ravichandran, D. and Hovy, E. 2002. Learning surface text patterns for a question answering system. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics (Philadelphia, PA). 41--47.

Digital Library

[227]

Resnik, P. 1995. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI, Montreal, P.Q., Canada). 448--453.

[228]

Resnik, P. 1997. Selectional preference and sense disambiguation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How&quest; (Washington, D.C.). 52--57.

[229]

Resnik, P. 2006. Word sense disambiguation in NLP applications. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY.

[230]

Resnik, P. and Smith, N. A. 2003. The Web as a parallel corpus. Computat. Ling. 29, 3, 349--380.

Digital Library

[231]

Resnik, P. and Yarowsky, D. 1997. A perspective on word sense disambiguation methods and their evaluation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How&quest; (Washington, D.C.). 79--86.

[232]

Resnik, P. and Yarowsky, D. 1999. Distinguishing systems and distinguishing senses: new evaluation methods for word sense disambiguation. J. Nat. Lang. Eng. 5, 2, 113--133.

Digital Library

[233]

Resnik, P. S., Ed. 1993. Selection and information: A class-based approach to lexical relationships, Ph.D. dissertation. University of Pennsylvania, Pennsylvania, Philadelphia, PA.

Digital Library

[234]

Richardson, S. D., Dolan, W. B., and Vanderwende, L. 1998. Mindnet: Acquiring and structuring semantic information from text. In Proceedings of the 17th International Conference on Computational Linguistics (COLING, Montreal, P.Q., Canada). 1098--1102.

Digital Library

[235]

Rigau, G., Rodriguez, H., and Agirre, E. 1998. Building accurate semantic taxonomies from monolingual MRDs. In Proceedings of the 17th International Conference on Computational Linguistics (COLING, Montreal, P.Q., Canada). 1103--1109.

Digital Library

[236]

Rivest, R. L. 1987. Learning decision lists. Mach. Learn. 2, 3, 229--246.

Digital Library

[237]

Roget, P. M. 1911. Roget's International Thesaurus, 1st ed. Cromwell, New York, NY.

[238]

Russell, S. and Norvig, P. 2002. Artificial Intelligence: A Modern Approach, 2nd ed. Prentice Hall, Englewood Cliffs, NJ.

Digital Library

[239]

Salton, G. 1968. Automatic Information Organization and Retrieval. McGraw-Hill, New York, NY.

Digital Library

[240]

Salton, G. and McGill, M. 1983. Introduction to Modern Information Retrieval. McGraw-Hill, New York, NY.

Digital Library

[241]

Sanderson, M. 1994. Word sense disambiguation and information retrieval. In Proceedings of the Special Interest Group on Information Retrieval (SIGIR, Dublin, Ireland). 142--151.

Digital Library

[242]

Sanderson, M. 2000. Retrieving with good sense. Inform. Retriev. 2, 1, 49--69.

Digital Library

[243]

Savova, G., Pedersen, T., Purandare, A., and Kulkarni, A. 2005. Resolving ambiguities in biomedical text with unsupervised clustering approaches. Res. rep. UMSI 2005/80. University of Minnesota Supercomputing Institute, Minneapolis, MN.

[244]

Schapire, R. E. and Singer, Y. 1999. Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 3, 297--336.

Digital Library

[245]

Schütze, H. 1992. Dimensions of meaning. In Supercomputing '92: Proceedings of the 1992 ACM/IEEE Conference on Supercomputing. IEEE Computer Society Press, Los Alamitos, CA. 787--796.

Digital Library

[246]

Schütze, H. 1998. Automatic word sense discrimination. Computat. Ling. 24, 1, 97--124.

Digital Library

[247]

Schütze, H. and Pedersen, J. 1995. Information retrieval based on word senses. In Proceedings of SDAIR'95 (Las Vegas, NV). 161--175.

[248]

Silber, H. G. and McCoy, K. F. 2003. Efficient text summarization using lexical chains. In Proceedings of the 5th International Conference on Intelligent User Interfaces (New Orleans, LA). 252--255.

Digital Library

[249]

Snow, R., Jurafsky, D., and Ng, A. Y. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21th Conference on Computational Linguistics (COLING-ACL, Sydney, Australia).

Digital Library

[250]

Snyder, B. and Palmer, M. 2004. The English all-words task. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 41--43.

[251]

Soanes, C. and Stevenson, A., Eds. 2003. Oxford Dictionary of English. Oxford University Press, Oxford, U.K.

[252]

Stevenson, M. and Wilks, Y. 2001. The interaction of knowledge sources in word sense disambiguation. Computat. Ling. 27, 3, 321--349.

Digital Library

[253]

Stokoe, C., Oakes, M. J., and Tait, J. I. 2003. Word sense disambiguation in information retrieval revisited. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Toronto, Onto., Canada). 159--166.

Digital Library

[254]

Strapparava, C., Gliozzo, A., and Giuliano, C. 2004. Pattern abstraction and term similarity for word sense disambiguation: Irst at Senseval-3. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3, Barcelona, Spain). 229--234.

[255]

Sussna, M. 1993. Word sense disambiguation for free-text indexing using a massive semantic network. In Proceedings of the 2nd International Conference on Information and Knowledge Base Management (Washington D.C.). 67--74.

Digital Library

[256]

Towell, G. and Voorhees, E. 1998. Disambiguating highly ambiguous words. Computat. Ling. 24, 1, 125--145.

Digital Library

[257]

Tratz, S., Sanfilippo, A., Gregory, M., Chappell, A., Posse, C., and Whitney, P. 2007. PNNL: A supervised maximum entropy approach to word sense disambiguation. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval, Prague, Czech Republic). 264--267.

[258]

Tsatsaronis, G., Vazirgiannis, M., and Androutsopoulos, I. 2007. Word sense disambiguation with spreading activation networks generated from thesauri. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI, Hyderabad, India). 1725--1730.

[259]

Tufis, D., Cristea, D., and Stamou, S. 2004. Balkanet: Aims, methods, results and perspectives. A general overview. Romanian J. Sci. Tech. Inform. (Special Issue on Balkanet) 7, 1-2, 9--43.

[260]

Tufis, D., Ion, R., and Ide, N. 2004. Fine-grained word sense disambiguation based on parallel corpora, word alignment, word clustering, and aligned WordNets. In Proceedings of the 20th International Conference on Computational Linguistics (COLING, Geneva, Switzerland).

Digital Library

[261]

Turing, A. M. 1950. Computing machinery and intelligence. Mind 54, 443--460.

[262]

van Dongen, S. 2000. Graph Clustering by Flow Simulation, Ph.D. dissertation. University of Utrecht, Utrecht, The Netherlands.

[263]

Veenstra, J., den Bosch, A. V., Buchholz, S., Daelemans, W., and Zavrel, J. 2000. Memory-based word sense disambiguation. Comput. Human. 34, 1--2.

[264]

Véronis, J. 2004. Hyperlex: Lexical cartography for information retrieval. Comput. Speech Lang. 18, 3, 223--252.

[265]

Véronis, J. and Ide, N. 1990. Word sense disambiguation with very large neural networks extracted from machine readable dictionaries. In Proceedings of the 13th International Conference on Computational Linguistics (COLING, Helsinki, Finland). 389--394.

Digital Library

[266]

Vickrey, D., Biewald, L., Teyssier, M., and Koller, D. 2005. Word sense disambiguation for machine translation. In Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP, Vancouver, B.C., Canada). 771--778.

Digital Library

[267]

Voorhees, E. M. 1993. Using wordnet to disambiguate word senses for text retrieval. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Pittsburgh, PA). 171--180.

Digital Library

[268]

Vossen, P., Ed. 1998. EuroWordNet: A Multilingual Database with Lexical Semantic Networks. Kluwer, Dordrecht, The Netherlands.

Digital Library

[269]

Weaver, W. 1949. Translation. In Machine Translation of Languages: Fourteen Essays (written in 1949, published in 1955), W. N. Locke and A. D. Booth, Eds. Technology Press of MIT, Cambridge, MA, and John Wiley & Sons, New York, NY, 15--23.

[270]

Widdows, D. and Dorow, B. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics (COLING, Taipei, Taiwan). 1--7.

Digital Library

[271]

Wilks, Y. 1975. Preference semantics. In Formal Semantics of Natural Language, E. L. Keenan, Ed. Cambridge University Press, Cambridge, U.K., 329--348.

[272]

Wilks, Y. and Slator, B. 1989. Towards semantic structures from dictionary entries. In Proceedings of the 2nd Annual Rocky Mountain Conference on AI (Boulder, CO). 85--96.

[273]

Wilks, Y., Slator, B., and Guthrie, L., Eds. 1996. Electric Words: Dictionaries, Computers and Meanings. MIT Press, Cambridge, MA, USA.

Digital Library

[274]

Wilks, Y. A., Fass, D. C., Guo, C.-M., McDonald, J. E., Plate, T., and Brian, B. M. 1990. Providing machine-tractable dictionary tools. Mach. Transl. 5, 99--154.

[275]

Yarowsky, D. 1992. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proceedings of the 14th International Conference on Computational Linguistics (COLING, Nantes, France). 454--460.

Digital Library

[276]

Yarowsky, D. 1993. One sense per collocation. In Proceedings of the ARPA Workshop on Human Language Technology (Princeton, NJ). 266--271.

Digital Library

[277]

Yarowsky, D. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (Las Cruces, NM). 88--95.

Digital Library

[278]

Yarowsky, D. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (Cambridge, MA). 189--196.

Digital Library

[279]

Yarowsky, D. 2000. Hierarchical decision lists for word sense disambiguation. Comput. Human. 34, 1-2, 179--186.

[280]

Yarowsky, D. and Florian, R. 2002. Evaluating sense disambiguation across diverse parameter spaces. J. Nat. Lang. Eng. 9, 4, 293--310.

Digital Library

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Index Terms

Word sense disambiguation: A survey
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Language resources
2. Theory of computation
  1. Logic

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 41, Issue 2

February 2009

248 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1459352

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Copyright © 2009 ACM.

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Publication History

Published: 23 February 2009

Accepted: 01 March 2008

Revised: 01 January 2008

Received: 01 December 2006

Published in CSUR Volume 41, Issue 2

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