A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages
This work addresses the problem of enabling natural language processing for under-resourced languages, but it is incremental as it focuses on initiating research rather than presenting new results.
The paper tackles the challenge of performing Word Sense Disambiguation for under-resourced languages by proposing the use of Word Sense Induction as a starting point, suggesting research directions to address the lack of lexical resources.
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.