  This paper explores techniques that focus on understanding and resolving
ambiguity in language within the field of natural language processing (NLP),
highlighting the complexity of linguistic phenomena such as polysemy and
homonymy and their implications for computational models. Focusing extensively
on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from
deep learning techniques to leveraging lexical resources and knowledge graphs
like WordNet. The paper introduces cutting-edge methodologies like word sense
extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy
by predicting new word senses. It examines specific applications in biomedical
disambiguation and language specific optimisation and discusses the
significance of cognitive metaphors in discourse analysis. The research
identifies persistent challenges in the field, such as the scarcity of sense
annotated corpora and the complexity of informal clinical texts. It concludes
by suggesting future directions, including using large language models, visual
WSD, and multilingual WSD systems, emphasising the ongoing evolution in
addressing lexical complexities in NLP. This thinking perspective highlights
the advancement in this field to enable computers to understand language more
accurately.
