The ability to identify which meaning of a word is activated by its use in a specific context is known as word sense disambiguation in natural language processing (NLP). It is an important NLP technique for determining a word's meaning in a specific context. Determining a word's precise usage in a sentence has numerous applications, and NLP systems frequently struggle to correctly identify words.
High-accuracy POS taggers can resolve Word's syntactic ambiguity. WSD (word sense disambiguation) refers to the problem of resolving semantic ambiguity. The resolution of semantic ambiguity is more difficult than the resolution of syntactic ambiguity. Word Sense Disambiguation essentially resolves the ambiguity that occurs when determining the meaning of a word when it is used in various contexts.
Methods of Word Sense Disambiguation (WSD):

• Dictionary-based or Knowledge-based Methods: These techniques mainly rely on lexical databases, dictionaries, and treasures. They do not rely on corpora evidence for ambiguity. The Lesk method was the first to use a dictionary. Lesk defines it as "measure overlap between sense definitions of the word and current context," which means identifying the correct sense for one word at a time. This sentence or paragraph's surrounding words make up the current context in this case.
• Supervised Methods: These techniques presuppose that the context can provide sufficient proof on its own to clarify the meaning. The terms "knowledge" and "reasoning" are not considered necessary in these approaches. As a collection of word "features," the context is depicted. Along with that, it contains details about the words around it.
• Semi-supervised Methods: Most word sense disambiguation algorithms employ semi-supervised learning strategies due to the dearth of training corpora. This is because semi-supervised methods employ both labeled and unlabeled data. These methods necessitate very little annotated text and a large amount of plain unannotated text.
• Unsupervised Methods: Due to their independence from manual labor, unsupervised methods have enormous potential to break the knowledge acquisition bottleneck.