Semantic vector learning for natural language understanding
Semantic vector learning for natural language understanding is a scholarly work, published in 2019 in ''Computer Speech and Language''. The main subjects of the publication include natural language, keyword extraction, embedding, computer science, word embedding, semantic computing, Word2vec, semantic technology, semantic similarity, glossary of archaeology, ranking function, artificial intelligence, natural language processing, information retrieval, frame, statistical machine translation, natural language understanding, semantic search, and Semantic compression. Toward this end, authors propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame.