Abstract
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.
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Notes
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In cases, when the training set is not available, SemCor [4], for example, may be used as a default training set.
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Moreover, we assume that all words (both in training and testing sets) that have no their embeddings are removed from the corresponding sets.
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WN30WNGWN30glConOneGraphRelSCOne-synsetEmbeddings.bin downloaded from http://bultreebank.org/en/DemoSem/Embeddings.
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It is clear, that the WNFS baseline for such extended test set will be also changed.
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Agre, G., Petrov, D., Keskinova, S. (2018). A New Approach to the Supervised Word Sense Disambiguation. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_1
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