The paper presents a new supervised approach for solving the all-words sense disambiguation (WSD) task, which allows avoiding the necessity to construct different specialized classifiers for disambiguating different target words. In the core of the approach lies a new interpretation of the notion ‘class’, which relates each possible meaning of a word to a frequency with which it occurs in some corpora. In such a way all possible senses of different words can be classified in a unified way into a restricted set of classes starting from the most frequent, and ending with the least frequent class. For representing target and context words the approach uses word embeddings and information about their part-of-speech (POS) categories. The experiments have shown that classifiers trained on examples created by means of the approach outperform the standard baselines for measuring the behavior of all-words WSD classifiers.
CITATION STYLE
Agre, G., Petrov, D., & Keskinova, S. (2018). A new approach to the supervised word sense disambiguation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11089 LNAI, pp. 3–15). Springer Verlag. https://doi.org/10.1007/978-3-319-99344-7_1
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