Intelligent recognition English translation system based on machine learning algorithm

Abstract

Since the beginning of the 21st century, in training, life and work, people have become more and more aware of the increasing frequency of communication between different languages. Whether they are idiosyncrasies or forms of communication, there are rigorous issues and increasing demands on the accuracy of translation communication. The main purpose of this paper is to design and research a system for intelligent recognition of English translation based on machine learning algorithms. Rapid advances in computing power, growth and adoption of the Internet, and multilingual knowledge bases in both countries and the United Nations have given us millions of bilingual institutions. More and more researchers are devoted to computational engineering training with success. Experiments show that the NP-sequencing pattern library plays the most important role, and its translation results are improved by 3 percentage points compared to the baseline system Moses. And about 2876 English terms in the whole test corpus can find the same syntactic structure in the pattern library, accounting for about 88.7% of the test corpus.