Menu
Home
Log in / Register
 
Home arrow Language & Literature arrow COGNITIVE APPROACH TO NATURAL LANGUAGE PROCESSING
Source

Conclusion

In this work, we presented a graph-based semi-supervised system for WSD, based on game theory and consistent labeling principles. Experimental results showed that our method improves the performance of conventional methods and that it requires a small amount of labeled points to outperform supervised systems. These systems require large corpora to be trained. These resources are difficult to create and are not suitable for domain specific tasks. Our system infers the meaning of a target word from a small amount of labeled data exploiting relational and contextual information. In fact, the information of a labeled point is not only used locally by near words but also propagated over the graph and used globally by the dynamical system obtained with our game theoretic framework.

Bibliography

[AGI 14] Agirre E., De Lacalle O.L., Soroa A., “Random walks for knowledge- based word sense disambiguation”, Computational Linguistics, vol. 40, no. 1, pp. 57-84, 2014.

[ARA 07] Araujo L., “How evolutionary algorithms are applied to statistical natural language processing”, Artificial Intelligence Review, vol. 28, no. 4, pp. 275-303, 2007.

[BIR 06] Bird S., “NLTK: the natural language toolkit”, Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 69-72, 2006.

[CHA 15] Chaplot D.S., Bhattacharyya P., Paranjape A., “Unsupervised word sense disambiguation using Markov random field and dependency parser”, AAAI, pp. 2217-2223, 2015.

[DAG 04] Dagan I., Glickman O., “Probabilistic textual entailment: generic applied modeling of language variability”, Proceeding of Learning Methods for Text Understanding and Mining, pp. 26-29, 2004.

[DEC 10] De Cao D., Basili R., Luciani M. et al, “Robust and efficient page rank for word sense disambiguation”, Proceedings of the 2010 Workshop on Graph- based Methods for Natural Language Processing, pp. 24-32, 2010.

[DIC 45] Dice L.R., “Measures of the amount of ecologic association between species”, Ecology, vol. 26, no. 3, pp. 297-302, 1945.

[EAS 10] Easley D., Kleinberg J., Networks, Crowds, and Markets, Cambridge University Press, Cambridge, 2010.

[FEL 98] Fellbaum C., WordNet, Wiley Online Library, 1998.

[HAR 54] Harris Z.S., “Distributional structure”, Word, vol. 10, nos. 2-3, pp. 146162, 1954.

[HAV 02] Haveliwala T.H., “Topic-sensitive PageRank”, Proceedings of the 11th International Conference on World Wide Web, pp. 517-526, 2002.

[HOL 75] Holland J.H., Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, 1975.

[JOR 02] Jordan M.I., Weiss Y., “Graphical models: probabilistic inference”, in Arbib M.A. (ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, 2002.

[KLE 02] Kleinberg J., Tardos E., “Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields”, Journal of the ACM (JACM), vol. 49, no. 5, pp. 616-639, 2002.

[LEE 92] Leech G., “100 million words of English: The British National Corpus (BNC)”, Language Research, vol. 28, no. 1, pp. 1-13, 1992.

[MAL 88] Mallery J.C., Thinking about foreign policy: finding an appropriate role for artificially intelligent computers, Masters Thesis, MIT, 1988.

[MAN 14] Manion S.L., Sainudiin R., “An iterative sudoku style approach to subgraph-based word sense disambiguation”, Proceedings of the Third Joint Conference on Lexical and Computational Semantics (* SEM 2014), pp. 40-50, 2014.

[MEN 14] Menai M., “Word sense disambiguation using evolutionary algorithms - application to Arabic language”, Computers in Human Behavior, vol. 41, pp. 92103, 2014.

[MIH 04] Mihalcea R., Tarau P., Figa E., “PageRank on semantic networks, with application to word sense disambiguation”, Proceedings of the 20th International Conference on Computational Linguistics, Association for Computational Linguistics, p. 1126, 2004.

[MOR 14] Moro A., Raganato A., Navigli R., “Entity linking meets word sense disambiguation: a unified approach”, Transactions of the Association for Computational Linguistics, vol. 2, pp. 231-244, 2014.

[MOS 89] Moscato P., “On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms”, Caltech Concurrent Computation Program, C3P Report, vol. 826, p. 1989, 1989.

[NAV 07a] Navigli R., Lapata M., “Graph connectivity measures for unsupervised word sense disambiguation”, International Joint Conference on Artificial Intelligence, pp. 1683-1688, 2007.

[NAV 07b] Navigli R., Litkowski K.C., Hargraves O., “SemEval-2007 task 07: coarse-grained English all-words task”, Proceedings of the 4th International Workshop on Semantic Evaluations, Association for Computational Linguistics, pp. 30-35, 2007.

[NAV 09] Navigli R., “Word sense disambiguation: a survey”, ACM Computing Surveys (CSUR), vol. 41, no. 2, p. 10, 2009.

[NAV 12a] Navigli R., Ponzetto S., “BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network”, Artificial Intelligence, vol. 193, pp. 217-250, 2012.

[NAV 12b] Navigli R., Ponzetto S., “Joining forces pays off: multilingual joint word sense disambiguation”, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1399-1410, 2012.

[PAG 99] Page L., Brin S., Motwani R. et al, The PageRank citation ranking: bringing order to the web, Technical report, Stanford InfoLab, 1999.

[PAL 01] Palmer M., Fellbaum C., Cotton S. et al, “English tasks: all-words and verb lexical sample”, The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems, pp. 21-24, 2001.

[PAN 02] Pantel P., Lin D., “Discovering word senses from text”, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 613-619, 2002.

[PAT 06] Patwardhan S., Pedersen T., “Using WordNet-based context vectors to estimate the semantic relatedness of concepts”, Proceedings of the EACL 2006 Workshop Making Sense of Sense-Bringing Computational Linguistics and Psycholinguistics Together, vol. 1501, pp. 1-8, 2006.

[PRA 07] Pradhan S.S., Loper E., Dligach D. et al, “SemEval-2007 task 17: English lexical sample, SRL and all words”, Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 87-92, 2007.

[REN 09] Rentoumi V., Giannakopoulos G., Karkaletsis V. et al., “Sentiment analysis of figurative language using a word sense disambiguation approach”, RANLP, pp. 370-375, 2009.

[SAM 11] Sammut C., Webb G.I., Encyclopedia of Machine Learning, Springer, Berlin, 2011.

[SIN 07] Sinhar S., Mihalcea R., “Unsupervised graph-based word sense disambiguation using measures of word semantic similarity”, ICSC, vol. 7, pp. 363-369, 2007.

[SMR 06] SmrZ P., “Using WordNet for opinion mining”, Proceedings of the Third International WordNet Conference, pp. 333-335, 2006.

[SNY 04] Snyder B., Palmer M., “The English all-words task”, Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 41-43, 2004.

[TAY 78] Taylor P.D., Jonker L.B., “Evolutionary stable strategies and game dynamics”, Mathematical Biosciences, vol. 40, no. 1, pp. 145-156, 1978.

[TON 06] Tong H., Faloutsos C., Pan J., “Fast random walk with restart and its applications”, Proceedings of the Sixth International Conference on Data Mining, pp. 613- 622, 2006.

[TRI 15a] Tripodi R., Pelillo M., “WSD-games: a game-theoretic algorithm for unsupervised word sense disambiguation”, Proceedings of SemEval-2015, pp. 329-334, 2015.

[TRI 15b] Tripodi R., Pelillo M., Delmonte R., “An evolutionary game theoretic approach to word sense disambiguation”, Proceedings of Natural Language Processing and Cognitive Science 2014, pp. 39-48, 2015.

[TRI 17] Tripodi R., Marcello P., “A Game-Theoretic Approach to Word Sense Disambiguation”, Computational Linguistics, vol. 1, p. 43, 2017.

[VAP 98] Vapnik V.N., Statistical Learning Theory, Wiley-Interscience, Hoboken, 1998.

[VIC 05] Vickrey D., Biewald L., Teyssier M. et al, “Word-sense disambiguation for machine translation”, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 771-778, 2005.

[VON 44] Von Neumann J., Morgenstern O., Theory of Games and Economic Behavior, Princeton University Press, Princeton, 1944.

[WEA 55] Weaver W., “Translation”, in Locke W., Booth D. (eds), Machine Translation of Languages, vol. 14, Technology Press, MIT, Cambridge, 1955.

[ZHO 10] Zhong Z., Ng H.T., “It makes sense: a wide-coverage word sense disambiguation system for free text”, Proceedings of the ACL 2010 System Demonstrations, Association for Computational Linguistics, pp. 78-83, 2010.

[ZHU 05] Zhu X., Lafferty J., Rosenfeld R., Semi-Supervised Learning with Graphs, Language Technologies Institute, School of Computer Science, Carnegie Mellon university, 2005.

 
Source
Found a mistake? Please highlight the word and press Shift + Enter  
< Prev   CONTENTS   Next >
 
Subjects
Accounting
Business & Finance
Communication
Computer Science
Economics
Education
Engineering
Environment
Geography
Health
History
Language & Literature
Law
Management
Marketing
Mathematics
Political science
Philosophy
Psychology
Religion
Sociology
Travel