Design of Vocabulary Query System in Computer Aided English Translation Teaching

Our research focuses on the design of a vocabulary query system in computer-aided English translation teaching. The system aims to provide an intelligent and personalized vocabulary query and recommendation function to help learners better understand and apply English vocabulary. In system design, we applied Bayesian networks to model and analyze semantic associations of vocabulary, and inferred possible meanings and synonyms of vocabulary by observing contextual and historical information. At the same time, we transform the Bayesian network into a factor graph and use the sum product method to efficiently calculate the marginal distribution of each vocabulary, in order to improve the computational efficiency of the system. Through the user’s query history and feedback information, we continuously optimize the Bayesian network model and provide users with recommended vocabulary or phrases related to the query vocabulary. Our system aims to provide accurate and targeted vocabulary query and recommendation functions, providing assistance for learners in English translation teaching. Through experimental evaluation, we have verified the effectiveness and positive feedback of the system. This study has made certain progress in the design of vocabulary query systems in computer-aided English translation teaching, and has further research and application potential.