English Translation Effect Evaluation System Based on Big Data

Abstract

In recent years, ontology has been widely used to model world knowledge at the conceptual level. In recent years, machine translation has been widely used. This paper focuses on the evaluation of machine translation system. This paper reviews and compares the types and standards of machine translation, the content of system evaluation, and the main methods of system evaluation. The experimental results show that a high-quality machine translation system must fully integrate linguistic knowledge and language neutral world knowledge. This paper introduces an ontology based English Chinese machine translation model system, which organizes concepts into a hierarchical structure and establishes rich conceptual connections among concepts. The accuracy of machine translation is improved by mapping words in a language to concepts in ontology. The experimental results show that the accuracy of machine translation can be improved by 10.8%.