Metaheuristic for Word Sense Disambiguation: a Review

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Word Sense Disambiguation (WSD) is the process of determining the exact sense of a particular word in accordance to the context in a computational manner. Such task plays an essential role in multiple fields of study such as Information Retrieval and Information Extraction. With the complexity of human language, WSD came up to solve the problem behind the ambiguity between senses in which a single word would yield different meaning. In this vein, determining the exact meaning of the certain word would facilitate the process of identifying the category of such text, accurate corresponding search results and providing an accurately summarized portion. Several approaches have been proposed for the WSD including statistical, semantic and machine learning techniques. This paper aims to provide a review of such approaches by tackling and categorizing the related works in accordance to the main types.

     


  • Keywords


    Word Sense Disambiguation, Machine learning Techniques, Semantic Similarity Measurement-Heuristic, Natural Language Processing.

  • References


      [1] Abualhaija, S., Miller, T., Eckle-Kohler, J., Gurevych, I., & Zimmermann, K.-H. (2016). Metaheuristic Approaches to Lexical Substitution and Simplification.

      [2] Abualhaija, S., & Zimmermann, K.-H. (2016). D-Bees: A novel method inspired by bee colony optimization for solving word sense disambiguation. Swarm and Evolutionary Computation, 27, 188-195.

      [3] Agirre, E., & Edmonds, P. G. (2007). Word sense disambiguation: Algorithms and applications (Vol. 33): Springer Science & Business Media.

      [4] Agirre, E., & Martinez, D. (2004). The basque country university system: english and basque tasks. Paper presented at the Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text.

      [5] Agirre, E., Martínez, D., de Lacalle, O. L., & Soroa, A. (2006). Two graph-based algorithms for state-of-the-art WSD. Paper presented at the Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing.

      [6] Agirre, E., & Rigau, G. (1995). A proposal for word sense disambiguation using conceptual distance. arXiv preprint cmp-lg/9510003.

      [7] Banerjee, S., & Pedersen, T. (2002). An adapted Lesk algorithm for word sense disambiguation using WordNet. Paper presented at the International Conference on Intelligent Text Processing and Computational Linguistics.

      [8] Banerjee, S., & Pedersen, T. (2003). Extended gloss overlaps as a measure of semantic relatedness. Paper presented at the IJCAI.

      [9] Bas, D., Broda, B., & Piasecki, M. (2008). Towards word sense disambiguation of Polish. Paper presented at the Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on.

      [10] Basile, P., De Gemmis, M., Gentile, A. L., Lops, P., & Semeraro, G. (2007). UNIBA: JIGSAW algorithm for word sense disambiguation. Paper presented at the Proceedings of the 4th International Workshop on Semantic Evaluations.

      [11] Başkaya, O., & Jurgens, D. (2016). Semi-supervised learning with induced word senses for state of the art word sense disambiguation. Journal of Artificial Intelligence Research, 55, 1025-1058.

      [12] Berleant, D. (1995). Engineering “word experts” for word disambiguation. Natural Language Engineering, 1(04), 339-362.

      [13] Bhingardive, S., & Bhattacharyya, P. (2017). Word Sense Disambiguation Using IndoWordNet The WordNet in Indian Languages (pp. 243-260): Springer.

      [14] Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine.[En línea]. Disponible en Web.

      [15] Chen, J., & Palmer, M. (2004). Chinese verb sense discrimination using an EM clustering model with rich linguistic features. Paper presented at the Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics.

      [16] Cowie, J., Guthrie, J., & Guthrie, L. (1992). Lexical disambiguation using simulated annealing. Paper presented at the Proceedings of the 14th conference on Computational linguistics-Volume 1.

      [17] Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391.

      [18] Dinu, G., & Kübler, S. (2007). Sometimes less is more: Romanian word sense disambiguation revisited. Paper presented at the Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP.

      [19] Dligach, D., & Palmer, M. (2008). Novel semantic features for verb sense disambiguation. Paper presented at the Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers.

      [20] Edmonds, P., & Cotton, S. (2001). SENSEVAL-2: overview. Paper presented at the The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems.

      [21] Fellbaum, C., Palmer, M., Dang, H. T., Delfs, L., & Wolf, S. (2001). Manual and automatic semantic annotation with WordNet. WordNet and Other Lexical Resources, 3-10.

      [22] Florian, R., Cucerzan, S., Schafer, C., & Yarowsky, D. (2002). Combining classifiers for word sense disambiguation. Natural Language Engineering, 8(04), 327-341.

      [23] Furnas, G. W., Deerwester, S., Dumais, S. T., Landauer, T. K., Harshman, R. A., Streeter, L. A., & Lochbaum, K. E. (1988). Information retrieval using a singular value decomposition model of latent semantic structure. Paper presented at the Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval.

      [24] Gale, W. A., Church, K. W., & Yarowsky, D. (1992). A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26(5-6), 415-439.

      [25] Gelbukh, A., Sidorov, G., & Han, S.-Y. (2003). Evolutionary approach to natural language word sense disambiguation through global coherence optimization. WSEAS Transactions on Computers, 2(1), 257-265.

      [26] Hassan, A. K. A., & Hadi, M. J. (2017a). PROPOSED MABC-SDAIR ALGORITHM FOR SENSE-BASED DISTRIBUTED ARABIC INFORMATION RETRIEVAL. Journal of Theoretical and Applied Information Technology, 95(3), 543.

      [27] Hassan, A. K. A., & Hadi, M. J. (2017b). Sense-Based Information Retrieval Using Fuzzy Logic and Swarm Intelligence.

      [28] Hausman, M. (2011). A genetic algorithm using semantic relations for word sense disambiguation. University of Colorado at Colorado Springs.

      [29] Hoste, V., Daelemans, W., Hendrickx, I., & van den Bosch, A. (2002). Dutch word sense disambiguation: Optimizing the localness of context. Paper presented at the Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions-Volume 8.

      [30] Hoste, V., Hendrickx, I., Daelemans, W., & van den Bosch, A. (2002). Parameter optimization for machine-learning of word sense disambiguation. Natural Language Engineering, 8(04), 311-325.

      [31] Ide, N., & Véronis, J. (1998). Introduction to the special issue on word sense disambiguation: the state of the art. Computational Linguistics, 24(1), 2-40.

      [32] Jain, A., Tayal, D. K., & Vij, S. (2017). A Semi-Supervised Graph-based Algorithm for Word Sense Disambiguation. Global Journal of Enterprise Information System, 8(2), 13-19.

      [33] Joshi, M., Pakhomov, S. V., Pedersen, T., & Chute, C. G. (2006). A comparative study of supervised learning as applied to acronym expansion in clinical reports. Paper presented at the AMIA.

      [34] Kilgarriff, A., & Palmer, M. (2000). Introduction to the special issue on SENSEVAL. Computers and the Humanities, 34(1), 1-13.

      [35] Kilgarriff, A., & Rosenzweig, J. (2000). Framework and results for English SENSEVAL. Computers and the Humanities, 34(1), 15-48.

      [36] Kübler, S., & Zhekova, D. (2009). Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity. Paper presented at the RANLP.

      [37] Le, C. A., & Shimazu, A. (2004). High WSD Accuracy Using Naive Bayesian Classifier with Rich Features. Paper presented at the PACLIC.

      [38] Lee, Y. K., & Ng, H. T. (2002). An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. Paper presented at the Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10.

      [39] Martínez, D. (2007). Supervised corpus-based methods for WSD Word Sense Disambiguation (pp. 167-216): Springer.

      [40] Martínez, D., Agirre, E., & Màrquez, L. (2002). Syntactic features for high precision word sense disambiguation. Paper presented at the Proceedings of the 19th international conference on Computational linguistics-Volume 1.

      [41] McCarthy, D. (2009). Word sense disambiguation: An overview. Language and Linguistics compass, 3(2), 537-558.

      [42] McCarthy, D., Koeling, R., Weeds, J., & Carroll, J. (2004). Finding predominant word senses in untagged text. Paper presented at the Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics.

      [43] Mihalcea, R. (2002). Instance based learning with automatic feature selection applied to word sense disambiguation. Paper presented at the Proceedings of the 19th international conference on Computational linguistics-Volume 1.

      [44] Mihalcea, R. (2006). Knowledge-based methods for WSD. Word Sense Disambiguation: Algorithms and Applications, 107-131.

      [45] Mihalcea, R. F. (2002). Word sense disambiguation with pattern learning and automatic feature selection. Natural Language Engineering, 8(04), 343-358.

      [46] Miller, G. A., Leacock, C., Tengi, R., & Bunker, R. T. (1993). A semantic concordance. Paper presented at the Proceedings of the workshop on Human Language Technology.

      [47] Miller, T., Biemann, C., Zesch, T., & Gurevych, I. (2012). Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation. Paper presented at the COLING.

      [48] Młodzki, R., Kopeć, M., & Przepiórkowski, A. (2012). Word Sense Disambiguation in the National Corpus Of Polish. Prace Filologiczne(LXIII), 155-166.

      [49] Mooney, R. J. (1996). Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. arXiv preprint cmp-lg/9612001.

      [50] Navigli, R. (2009). Word sense disambiguation: A survey. ACM Computing Surveys (CSUR), 41(2), 10.

      [51] Ng, H. T., & Lee, H. B. (1996). Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach. Paper presented at the Proceedings of the 34th annual meeting on Association for Computational Linguistics.

      [52] Nguyen, K.-H., & Ock, C.-Y. (2013). Word sense disambiguation as a traveling salesman problem. Artificial Intelligence Review, 1-23.

      [53] Pedersen, T. (2001). A decision tree of bigrams is an accurate predictor of word sense. Paper presented at the Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies.

      [54] Pedersen, T. (2010). Information content measures of semantic similarity perform better without sense-tagged text. Paper presented at the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

      [55] Pedersen, T., Patwardhan, S., & Michelizzi, J. (2004). WordNet:: Similarity: measuring the relatedness of concepts. Paper presented at the Demonstration papers at HLT-NAACL 2004.

      [56] Ponzetto, S. P., & Navigli, R. (2010). Knowledge-rich word sense disambiguation rivaling supervised systems. Paper presented at the Proceedings of the 48th annual meeting of the association for computational linguistics.

      [57] Purandare, A., & Pedersen, T. (2004). Improving word sense discrimination with gloss augmented feature vectors. Paper presented at the Workshop on Lexical Resources for the Web and Word Sense Disambiguation.

      [58] Raganato, A., Camacho-Collados, J., & Navigli, R. (2017). Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison. Paper presented at the Proc. of EACL.

      [59] Ramakrishnan, G., Prithviraj, B., & Bhattacharyya, P. (2004). A gloss-centered algorithm for disambiguation. Paper presented at the Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text.

      [60] Schütze, H. (1998). Automatic word sense discrimination. Computational linguistics, 24(1), 97-123.

      [61] Taghipour, K., & Ng, H. T. (2015). Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains. Paper presented at the HLT-NAACL.

      [62] Torres, S., & Gelbukh, A. (2009). Comparing similarity measures for original WSD lesk algorithm. Research in Computing Science, 43, 155-166.

      [63] Vasilescu, F., Langlais, P., & Lapalme, G. (2004). Evaluating Variants of the Lesk Approach for Disambiguating Words. Paper presented at the LREC.

      [64] Veenstra, J., Van den Bosch, A., Buchholz, S., & Daelemans, W. (2000). Memory-based word sense disambiguation. Computers and the Humanities, 34(1-2), 171-177.

      [65] Véronis, J. (2004). Hyperlex: lexical cartography for information retrieval. Computer Speech & Language, 18(3), 223-252.

      [66] Vial, L., Tchechmedjiev, A., & Schwab, D. (2017). Comparison of Global Algorithms in Word Sense Disambiguation. arXiv preprint arXiv:1704.02293.

      [67] Wiriyathammabhum, P., Kijsirikul, B., Takamura, H., & Okumura, M. (2012). Applying deep belief networks to word sense disambiguation. arXiv preprint arXiv:1207.0396.

      [68] Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. Paper presented at the Proceedings of the 32nd annual meeting on Association for Computational Linguistics.

      [69] Yarowsky, D., & Florian, R. (2002). Evaluating sense disambiguation across diverse parameter spaces. Natural Language Engineering, 8(4), 293.

      [70] Zavrel, J., Degroeve, S., Kool, A., Daelemans, W., & Jokinen, K. (2000). Diverse classifiers for NLP disambiguation tasks comparisons, optimization, combination, and evolution. Paper presented at the Twente Workshops on Language Technology.

      [71] Zhang, C., Zhou, Y., & Martin, T. (2008). Genetic word sense disambiguation algorithm. Paper presented at the Intelligent Information Technology Application, 2008. IITA'08. Second International Symposium on.

      [72] Zhao, G. Z., & Zuo, W. L. (2014). Semi-Supervised Word Sense Disambiguation via Context Weighting. Paper presented at the Advanced Materials Research.


 

View

Download

Article ID: 20586
 
DOI: 10.14419/ijet.v7i3.20.20586




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.