现有大多数计算机辅助词汇学习系统鲜少关注词汇语义相关性对单词识记的影响作用.然而,根据词汇学习理论,词汇学习不同环节应当采用不同策略处理存在语义相关的词汇.为提升计算机辅助词汇学习系统的科学性与智能化,文章引入自然语言处理领域中的词向量技术,用于自动计算单词之间的语义相似性和构建词汇语义网络,并结合学习者行为日志,对词汇学习系统的运行逻辑和教学流程进行了改进设计.研究表明,词向量技术对增强计算机辅助词汇学习系统的智能水平具有良好的应用价值.
Little attention has been paid,in previous architecture design of Computer Assisted Vocabulary Learning(CAVL)sys-tems,to the semantic connectedness among target words and its possible effect on learning.However,vocabulary learning researches indi-cate that different strategies should be adopted to process semantically related words at different stages of vocabulary learning.To improve current CAVL systems,this paper formulated and designed a scientific and intelligent learning scheme for CAVL system through word-em-bedding based word semantic similarity computation and lexical semantic network construction,as well as the tracking of learner usage be-haviors.The study demonstrates that word embedding technology has great potential in application to CAVL.