Study On Several Key Problems In The Machine Translation System Based On Semantic Language

Up to now,there isn't a full automatic and high quality machine translation system being developed yet.Its difficulty mainly lies in the complexity of language knowledge and our limited understanding of the rules of human languages.At present the study on machine translation not only is constraint to semantic and syntactic analysis in single sentence,but needs to explore the inherent law of the language and context information like sentences, paragraphs,texts and styles,etc.Multi-language machine translation based on Semantic Language analyzes the source language sentences semantically with reference to the Semantic Element Base,and converts the source language to the form of the target language to realize translation.The machine translation system is composed of two parts:one is a unified multi-language machine translation software;the other is a high-quality,expandable, complete,free-discardable,free-of-repetition and free-of-abnormal-ambiguity multi-language Semantic Element Base.But limited to the corpora and influenced by complex and flexible language phenomena,the present Semantic Element Base is not perfect enough(incomplete and wrong Semantic Elements and their representations) to realize entirely right translation for all the sentences.Furthermore there still exist some difficulties need to be settled in the system.To improve translation quality and get correct translation,a series of researche(?) have been taken to solve three key problems among all and they are mainly carried out in the following three aspects:(1) Study on one of the key problems in the machine translation system based on semantic language:classifiers.Firstly based on the theory of semantic element in unified linguistics,a new unified classification of English and Chinese nominal classifiers was proposed.Different semantic element representations of classifiers in English and Chinese have the same semantic type of classifiers.The noun-classifier phrases were formalized into English and Chinese semantic element representations respectively.Then the nouns collocated with the formalized classifiers were represented semantically by the lexical definition in the HowNet,and a Formalized Classifier-Noun Collocation Rule Base was constructed.Finally a Chinese formalized classifier selection method based on lexical examples and Collocation Rule Base was proposed and realized. (2) Study on one of the key problems in the machine translation system based on semantic language:semantic disambiguation and Chinese translation of English prepositions.Firstly based on the theory of Semantic Element and the characteristics of phrases and sentences with prepositions,the Semantic Pattern Bases of English prepositions(special Semantic Element Representation Bases for prepositions) were presented.Then the translation process of prepositions was in two steps.One was getting English complete semantic pattern by semantic analysis based on Semantic Pattern Base,the other was deploying into Chinese complete semantic pattern and Chinese representation.Finally three semantic analysis methods peculiar to prepositions in semantic analysis step were proposed. The first method was based on Link Grammar and semantic pattern.The parsing results by the Link Grammar Parser were matched with the correspondence between the phrases and sentences with prepositions and the connectors to get the grammatical structure.The second method was based on semantic pattern decomposition.The four basic forms of phrases and sentences with prepositions were decomposed into simple semantic patterns.The third method was based on semantic pattern extending.The verbs,nouns and adjectives were extended around the prepositions to get real extending form.The test results prove that the Semantic Pattern Bases of English prepositions and the three semantic analysis methods are effective on resolving semantic analysis and Chinese translation of English prepositions in the machine translation system based on semantic language.(3) Study on one of the key problems in the machine translation system based on semantic language:Chinese-English temporal transfer.Firstly based on the analysis of bilingual sentence pairs and summarizing the law of temporal expressions,a new classification method of Chinese temporal information was proposed in the light of the reversal mapping from sixteen English tense and aspect to Chinese temporal expressions.This classification method can effectively avoid the drawback of complexity and overlapping of traditional classification.Then the concept of Temporal Pattern was presented to formalize each type of the temporal information,and the Temporal Pattern Base was constructed.Finally the temporal analysis and translation of Chinese sentences in the system were resolved by combining temporal analysis algorithm of Chinese simple sentence,conjunction-marked sentence and analogous subjunctive mood sentence with context rules.It provides a feasible solution for temporal transfer in the machine translation system based on semantic language.