Representation of Semantic Word Embeddings Based on SLDA and Word2vec Model

To solve the problem of semantic loss in text representation, this paper proposes a new embedding method word representation space called wt2svec based on supervised latent Dirichlet allocation (SLDA) and Word2vec. It generates global topic vector utilizing SLDA which can discover information through topics whole document set. gets local The is obtained by combining with information. Additionally, named doc2svec generated. experimental results different datasets show that model obviously promote accuracy similarity words, improve performance categorization compared