Semantic Similarity Models
Semantic similarity models are computational techniques in natural language processing (NLP) that measure how similar two pieces of text are in meaning, rather than just surface-level word overlap. They use machine learning, often based on deep neural networks like transformers, to encode text into dense vector representations (embeddings) and compute similarity scores such as cosine similarity. These models are essential for tasks like information retrieval, duplicate detection, and semantic search.
Developers should learn semantic similarity models when building applications that require understanding text meaning, such as chatbots for matching user queries to responses, recommendation systems for finding related content, or plagiarism detection tools. They are particularly useful in NLP pipelines where traditional keyword-based methods fail to capture contextual nuances, enabling more accurate and human-like text analysis in domains like customer support, e-commerce, and academic research.