Document Embedding

Document Embedding is a technique that converts documents into fixed-length vectors, aiming to capture the semantic information and contextual relationships within the documents. By mapping documents to vector representations in a high-dimensional space, Document Embedding enables more efficient text similarity calculations, classification, and clustering tasks, thereby enhancing the performance and accuracy of natural language processing systems. This technology has significant application value in areas such as information retrieval, sentiment analysis, and topic modeling, effectively promoting the intelligent processing and analysis of textual data.