Semantic Similarity
Semantic similarity is a measure of how closely related the meanings of two pieces of text, words, or concepts are, based on their underlying semantic content rather than just surface-level features like syntax or spelling. It is a core concept in natural language processing (NLP) and computational linguistics, used to quantify the degree of equivalence in meaning. Techniques for measuring semantic similarity range from traditional lexical methods to modern deep learning approaches like word embeddings and transformer models.
Developers should learn semantic similarity when working on NLP applications such as search engines, recommendation systems, chatbots, or text classification, where understanding contextual meaning is crucial. It is essential for tasks like duplicate detection, query expansion, and semantic search to improve accuracy and user experience. Knowledge of semantic similarity also aids in building more intelligent AI systems that can interpret and respond to human language more naturally.