Word Sense Disambiguation for 158 Languages using Word Embeddings Only

Varvara Logacheva; Denis Teslenko; Artem Shelmanov; Steffen Remus; Dmitry Ustalov; Andrey Kutuzov; Ekaterina Artemova; Chris Biemann; Simone Paolo Ponzetto; Alexander Panchenko

arXiv · 2003.06651

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

Varvara Logacheva·Denis Teslenko·Artem Shelmanov·Steffen Remus·Dmitry Ustalov·Andrey Kutuzov·Ekaterina Artemova·Chris Biemann·Simone Paolo Ponzetto·Alexander Panchenko

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Abstract

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages. Models and system are available online.

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Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko. 2020-03-14. Word Sense Disambiguation for 158 Languages using Word Embeddings Only. https://arxiv.org/abs/2003.06651

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