A review of word-sense disambiguation methods and algorithms: Introduction
Татьяна Викторовна Каушинис, Александр Николаевич Кириллов, Никита Иванович Коржицкий, Андрей Анатольевич Крижановский, Александр Пилинович, Ирина Александровна Сихонина, Анна Михайловна Спиркова, Valentina Starkova, Татьяна Владимировна Степкина, Станислав Сергеевич Ткач, Юлия Васильевна Чиркова, Алексей Леонидович Чухарев, Дарья Сергеевна Шорец, Дарья Юрьевна Янкевич, Екатерина Александровна Ярышкина, Татьяна Викторовна Каушинис, Alexander Kirillov, Nikita Korzhitsky, Andrew Anatoliyevich Krizhanovsky, Aleksander Pilinovich · Proceedings of the Karelian Research Centre of the Russian Academy of Sciences · 2015
The word-sense disambiguation task is a classification task, where the goal is to predict the meaning of words and phrases with the help of surrounding text. The purpose of this short review is to acquaint the reader with the general directions of word-sense disambiguation methods and algorithms. These approaches include the following groups of methods: neural network, machine learning meta-algorithms (AdaBoost), lexical chain computation, methods based on Bayes' theorem, context clustering and words clustering algorithms. The experimental comparison of different algorithms concludes this review. This paper is licensed under the CC Attribution license.