State of the Art Analysis of Word Sense Disambiguation

Word Sense Disambiguation (WSD) is a computational activity in natural language processing (NLP) that aims to determine the meaning of a word in its context by choosing the most appropriate meaning or definition from a predefined set of possibilities. WSD is essential for many applications such as machine translation, information retrieval, text classification, question answering, summarization, sentiment analysis, and word processing. WSD is a difficult task, especially in cases where words have multiple meanings or the context is ambiguous, which can lead to difficulties in choosing the correct meaning of a word. The WSD has applied many strategies to different datasets and corpora. This study used a knowledge-based approach and machine learning techniques to classify WSD algorithms. Each category is examined in-depth, along with an explanation of its associated algorithms. The paper reviews various approaches, and resources, used in WSD. The survey includes papers from various journals and discusses recent trends and competitions in the field, as well as future directions for research.