Research on Intelligent Recognition Model of English Translation Based on Nonlinear Dimensionality Reduction IC Features

Abstract

This paper first aligns English translations with other language texts to build an English translation corpus, then trains the model for the pending translations to obtain the final target model, then enumerates all possible combinations of source language phrases and target language phrases, and filters out unsatisfied phrase translation pairs to achieve phrase extraction. And the translation model is non-linearly dimensionalized to reduce the complexity of the operation process. Finally, the dimensionality reduction effect of the data and the effect of the model translation are analyzed. The results show that the cumulative contribution rate of the t-SNE algorithm is over 95%, which can guarantee no loss of translation information. The translation accuracy of this paper’s algorithm on each language block is basically 85%-90%, the recall rate is above 85%, and the F-value is above 82%. It indicates that the method in this paper can be well adapted to the requirements of intelligent recognition of English translation.