Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation

14 May 2019Loïc VialBenjamin LecouteuxDidier Schwab

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduces the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision... (read more)

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