Study On English-Chinese Machine Translation Based On Semantic Language Theory

Machine Translation is always a worldwide difficulty. Its difficulty mainly lies in the complexity of language knowledge and our limited understanding of the rules of human languages. In the process of exploring machine translation, we have realized that high quality translation requires semantic analysis and understanding of natural languages. Based on Semantic Language theory, a series of researches have been taken to solve important and difficult problems of English-Chinese machine translation. They are mainly carried out in the following three aspects:(1) Study on the kinds of English words and expressions which are often misunderstood and mistranslated.Based on quantities of examples, a study about the kinds of English words and expressions which are often misunderstood by Chinese people is reported. Concepts of Semantic Module and the Refraction Phenomenon in natural languages are presented. Seven situations of the Refraction Phenomenon between the English language and the Chinese language are concluded. Comparisons of the kinds of English words and expressions are made among the seven situations. Nine cultural factors causing the Refraction Phenomenon are concluded. Experiment results show us that the English maps of the Refraction Phenomenon are the most-easily misunderstood English words and expressions. The conclusion can be applied to the machine translation to improve the correction rate of machine translation.(2) Study on the metaphor recognition and Chinese translation of English words.The concept of Semantic Grammar Pattern, Semantic grammar pattern extraction rules and a serious of semantic grammar pattern extraction methods are presented. Researching on the most frequently-used English Human Body Words, the Variable Representation Base, Set Phrases Set and Semantic Grammar Pattern Set of English Human Body Words are constructed. The Semantic Grammar Pattern Set includs Grammar Metaphor Pattern Set, Lexical Metaphor Pattern Set, Literal Meaning Pattern Set and Semantic Grammar Pattern Set of English Phrases and Sentences, etc.. Based on them, a new metaphor recognition and Chinese translation algorithm was presented. The test results prove that the metaphor recognition and Chinese translation algorithm are effective on resolving metaphor recognition and Chinese translation of English words. (3) Study on the induction and application of translation templates with syntactico-semantic type constraints.The translation template with syntactico-semantic typed variables and a learning technique that inducing translation templates with syntactico-semantic typed variables are presented. The syntactico-semantic types of variables are induced by using syntactico-semantic type trees. The test results prove that using translation templates with syntactico-semantic typed variable, the BLEU value are higher than using translation templates with grammar typed variable in both English-Chinese and Chinese-English machine translation, respectively 0.08 and 0.05. The translation templates with syntactico-semantic typed variables can be implemented as a part of Multi-language machine translation system. And it also can be implemented as a part of other EBMT systems. Since many translation templates with syntactico-semantic typed variables are semantic elements, the extraction of translation templates with syntactico-semantic type constraints is helpful to enrich our semantic element representation base.