Application of Stochastic Matrix Model with Improved GLR Algorithm in English Translation Studies

Abstract

The rapid development of today’s society is accompanied by the explosive growth of information data; in the process of information transmission, language is a very important carrier. Among all kinds of communication languages, English always occupies an important position and is one of the most commonly used languages in social life. Therefore, the practical significance of English education is self-evident. With the popularization of the Internet, intelligent phrase recognition in machine translation is the key technology. With the help of natural language processing technology, an English translation corpus can be built to accurately mark the parts of speech of short words, and phrase recognition technology is used to correct grammatical ambiguity effectively. Structural ambiguity is a difficult problem in the field of English translation. Based on the random matrix model of the improved GLR algorithm, phrase structure labelling is constructed through the phrase corpus. Revised annotation can effectively improve the accuracy of academic translation, and intelligent English translation is realized through recognition technology. Simulation experiments verify the effectiveness of the model, and the results show that the English translation intelligent recognition model has a high proofreading accuracy. When the value of P is 0.95, the high accuracy can be retained to the maximum and the efficiency and feasibility of improving the GLR algorithm in machine translation can be improved.