Deep Semantic Similarity Learning is a deep learning approach used to measure and understand how similar two pieces of text are based on meaning. It converts inputs such as queries, sentences, or documents into dense vector representations using neural networks. These vectors allow systems to compare semantic similarity instead of relying on exact keyword matches. This improves performance in search engines, recommendation systems, and NLP applications. It helps capture context, intent, and relationships between words more effectively. The model also improves continuously by learning from large-scale user interaction data. It is widely used in modern AI-powered retrieval and matching systems.
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