Semantic similarity measures the likeness of meanings between two pieces of text, not just the words or syntax. It uses natural language processing techniques to determine how close in meaning two texts are, often leveraging word embeddings like Word2Vec or techniques like BERT. This is useful in applications like paraphrase detection, question answering, and document clustering where understanding the underlying meaning is crucial.