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Introduction

In this work we present a Word Sense Disambiguation (WSD) engine that integrates a Transformer-based neural architecture with knowledge present in WordNet, the resource from which the sense inventory is taken from.

Model

The architecture is composed of ELMo embeddings plus a TransformerXL (x3) on top with a final dense layer for tagging each word with the right lemma, pos, and sense identifier.

To incorporate lexical knowledge at evaluation time where we score each possible sense of a word with different scores:

• the semantic similarity of the context with the gloss of the sense and it's direct hypernyms and hyponyms.
• the accumulated probabilities of BERT language model for the lemma names of the synset and of its direct hypernyms and hyponyms.