Semantic similarity is a measure of how closely related two pieces of text or concepts are in meaning. It is used in various natural language processing tasks such as information retrieval, text mining, and machine learning. The goal of semantic similarity is to quantify the degree of similarity between words, sentences, or documents based on their semantic content rather than just their lexical similarity. There are multiple methods to measure semantic similarity, including knowledge-based approaches using ontologies and semantic networks, distributional approaches based on the co-occurrence of words in large text corpora, and embedding-based approaches using neural network models to represent words or phrases in a continuous vector space. Overall, semantic similarity research aims to improve the performance of various NLP tasks by enabling computers to better understand and process human language in a more context-aware and nuanced manner.