Разрешение лексической многозначности (WSD) - это задача выбора между разными значениями слов и словосочетаний в словаре в зависимости от контекста.
В статье представлен краткий обзор методов и алгоритмов разрешения лексической многозначности. Эти методы используют различный математический и алгоритмический аппарат для решения WSD-задачи: нейронные сети, адаптивные алгоритмы улучшения точности обучения (AdaBoost), построение лексических цепочек, методы на основе применения теоремы Байеса и методы кластеризации контекстных векторов и семантически близких слов. Завершает работу сравнение разных алгоритмов решения WSD-задачи. Статья распространяется на правах свободной лицензии "CC Attribution".

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Mooney R. J., Califf M. E. Induction of FirstOrder Decision Lists: Results on Learning the Past Tense of English Verbs / Department of Computer Sceinces, University of Texas, Austin, TX 78712-1188, 1995.

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Pedersen T., Bruce R. Distinguishing word senses in untagged text // Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997.

Purandare A., Pedersen T. Improving word sense discrimination with gloss augmented feature vectors // Workshop on Lexical Resources for the Web and Word Sense Disambiguation, 2004. P. 123–130.

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Schapire R. E. Using output codes to boost multiclass learning problems. In Machine Learning // Proceedings of the Fourteenth International Conference, 1997. P. 313–321.

Schütze H. Automatic Word Sense Discrimination // Computational Linguistics, 1998. Vol. 24, N. 1.

SenseClusters. URL: http://senseclusters.sourceforge.net (дата обращения: 24.04.2015).

UMLS Terminology Services (UTS). URL: http://wsd.nlm.nih.gov/ template (дата обращения: 22.04.2015).

Veronis J., Ide N. Word sense disambiguation with very large neural networks extracted from machine readable dictionaries // Proceedings of the 13th International Conference on Computational Linguistics. Helsinki, 1990. P. 389–394. doi:10.3115/997939.998006

Waltz D. L., Pollack J. B. Massively parallel parsing: a strongly interactive model of natural language interpretation // Cognitive science, 1985. N. 9. P. 51–74. doi: 10.1207/s15516709cog0901_4

Weeber M., Mork J. G., Aronson A. R. Developing a test collection for biomedical word sense disambiguation // In Proceedings of the AMIA Symposium, Chicago, 2001. P. 746–750.

Zhao Y., Karypis G. Evaluation of hierarchical clustering algorithms for document datasets // In Proceedings of the 11th International Conference on Information and Knowledge Management, McLean, VA, 2002. P. 515–524. doi:10.1145/584792.584877