DSSM (Deep Structured Semantic Model) for NLP is a deep learning approach used to understand and match the meaning of natural language text such as sentences, queries, and documents. It converts text into dense vector representations so that semantic similarity is measured based on meaning rather than exact keyword overlap. This helps NLP systems handle ambiguity, varied phrasing, and context more effectively. DSSM improves tasks like text matching, search, and question answering in natural language processing. It is widely used in chatbots, search engines, and recommendation systems. The model also learns from data patterns to improve language understanding over time.
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