An Efficient Construction Method for Matrix Decomposition-Based Natural Language Processing Models in Low-Dimensional Embedding Space

Natural language processing (NLP) is developing very rapidly in the field of artificial intelligence, and has become an important direction in the development of computer science field and artificial intelligence industry.In this paper, in order to realize the efficient construction of natural language processing model in low-dimensional embedding space, firstly, a word vector learning model is constructed based on matrix decomposition for word vectors in natural language processing.On this basis, in order to further realize the efficient construction of natural language processing models, this paper designs the Semantic Discarding Network (SDN) and Semantic Fusion Alignment Method (SFA) for the problem of interfering semantics of the model and the problem of a single way of fusion of local inference results.Finally, the SDF-NN natural language processing model is proposed and a multi-view subspace clustering (DLTE) method based on deep low-rank tensor embedding is proposed.The results of the research experiments show that the average performance index of this paper's word vector model for each task in three corpora ranges from 71.55 to 89.11, and the performance is stable and the time overhead in the three corpora is 3.93, 7.29, and 13.42 minutes, respectively, and the speed of the model has been significantly improved and the overall performance is better.In addition, the natural language processing model (SDF-NN) constructed in this paper achieves the best performance in the comparison test with strong competitiveness, which further validates the performance of the matrix decomposition-based natural language processing model in this paper, and provides the method and direction for its efficient construction in low-dimensional embedding space.