A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages

TL;DR

This paper reviews the current state of Word Sense Induction (WSI) as a promising approach to improve Word Sense Disambiguation (WSD) for under-resourced languages, highlighting research directions and challenges.

Contribution

It provides a comprehensive overview of WSI techniques and proposes new research avenues for applying WSD in under-resourced language contexts.

Findings

WSI can help address resource scarcity in WSD for under-resourced languages.

Identifies key challenges and potential solutions in applying WSI to low-resource languages.

Suggests future research topics to advance WSD in under-resourced language settings.

Abstract

Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of a polysemous word in a sentence, is a fundamental task in Natural Language Processing (NLP). Progress in this approach to WSD opens up many promising developments in the field of NLP and its applications. Indeed, improvement over current performance levels could allow us to take a first step towards natural language understanding. Due to the lack of lexical resources it is sometimes difficult to perform WSD for under-resourced languages. This paper is an investigation on how to initiate research in WSD for under-resourced languages by applying Word Sense Induction (WSI) and suggests some interesting topics to focus on.