Improving Word Sense Disambiguation By Adopting Refined Algorithms

Word sense disambiguation (WSD) is an important subfield of Natural Language Processing (NLP) that helps in determining the correct sense of a word in a sentence based on the context. Certain words have different meanings depending on their context, and misinterpretations of these words can lead to ambiguity. Ambiguity can create uncertainty about the meaning of a sentence, leading to misunderstandings in the communication. This paper explores different WSD approaches, focusing their efficiency, specifically in terms of accuracy and computational resources. While significant progress has been made, there are still some challenges including shortage of labeled datasets, especially in low resource languages. This highlights the necessity for advancements in WSD techniques to improve their effectiveness.