Research on semantic association vector MSAV feature selection based on Sal-F algorithm.

The traditional way of lexical interpretation is to use some words to explain other words. These semantic information cannot be used to solve the problem of natural language processing oriented to the physical situation. Based on this, this paper proposes a new method based on the feature vector Word semantic representation model and its corresponding learning algorithm Sal-F. This algorithm applies the idea of cross-scenario learning to the alignment of "word-shape features" and uses the result of feature selection under MSAV to construct a visual semantic model Lsm-G, A semantic dictionary based on graph features is constructed. Using machine-oriented evaluation method, the average selection accuracy of this algorithm is about 70%, and the accuracy of sentence selection is up to 16%. [ABSTRACT FROM AUTHOR]