Learning Semantic Representations

"Learning Semantic Representations" refers to the process of extracting and constructing semantic representations of words, phrases, and sentences from large amounts of text data using deep learning and other techniques. The goal is to generate vector representations that accurately reflect the semantic characteristics of linguistic units, thereby enhancing the model's understanding and generalization capabilities in natural language processing tasks. Its application value is extensive, including but not limited to sentiment analysis, machine translation, question answering systems, and text classification, which can significantly improve the intelligence level and user interaction experience of the system.