Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021)

Abstract: In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources. However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context. In this paper, we investigate the contribution of both word-level and sense-level global context of an ambiguous word for disambiguation. Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system’s performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.

## introduction word sense disambiguation (wsd) is aimed at selecting the correct sense for a word given its context. potential senses of a word are from a sense inventory such as wordnet @xcite . wsd can be classified into lexical sample wsd and all-words wsd. the former focuses on disambiguating some particular words in many sentences, while the latter conducts wsd on every ambiguous word in the provided text. the nature of all-words wsd allows the task to be more compatible to downstream applications. nevertheless, the task becomes more difficult @xcite while it also provides more context information (rather than a single sentence). ## conclusion in this paper, we have presented coe, a contextoriented embedding technique for similaritybased wsd systems. it takes better advantage of both word-level and sense-level information from the document where an ambiguous word appears. experiments have shown that the proposed method can enhance a system's performance on all-words wsd by relatively large margins. the ablation study has shown the contribution of each proposed factor. the source code will be made available at github for further development.