Technology Application of Achievement Orientation Based on Semantic Key Big Data in Higher Vocational English Teaching

Abstract

Taking English vocabulary learning as an example, this paper expounds the application of achievement orientation based on semantic key big data in Vocational English teaching. This paper also proposes an encyclopedic big data organization framework for online English education resources. Vocabulary is not only the foundation of English learning, but also the premise of improving the four basic skills of English listening, speaking, reading and writing. In traditional English teaching, new words are usually presented in the form of vocabulary. From the perspective of lexical meaning, words are presented in the textbook in isolation, and no background information can be used to anchor the semantic domain of words. The result of this practice is that learners lack deep understanding of vocabulary and the effect of vocabulary learning is not ideal. Advanced linguistic theories and achievements can guide language practice and promote teaching effect. This paper attempts to demonstrate that FrameNet can effectively distinguish the semantic differences of English vocabulary based on case study from the problems that online English education is faced with, such as the huge amount of resources which are difficult to organize, the lack of unified description between heterogeneous resources which are difficult to access across platforms, the lack of relationship between resources which are difficult to retrieve. This paper studies the influence of semantic features of English vocabulary on syntactic representation, and summarizes the ideas and patterns of using FrameNet to extract semantic information and syntactic information. In this paper, through lightweight resource links, we can achieve the collection of big data of educational resources in the same platform. Based on knowledge mapping, the unified semantic description and relationship description of English education resources are realized. This method eliminates the ambiguity of English resources and realizes the semantic level knowledge point association between resources.