基于神经网络词嵌入模型的单词联想记忆系统设计  

Design of word association memory system based on neural network word embedding model

作　　者：王彬彬 李强 李若宇 Wang Binbin;Li Qiang;Li Ruoyu(School of Computer Science,Huainan Normal University,Huainan 232038,China)

机构地区：[1]淮南师范学院计算机学院,安徽淮南232038

出　　处：《无线互联科技》2023年第12期33-36,共4页Wireless Internet Technology

基　　金：安徽省高校自然科学研究重点项目,项目名称:基于神经网络的大规模认知诊断实时参数估计方法研究,项目编号:2022AH051576;淮南师范学院科研项目,项目名称:基于时序数据的动态认知诊断模型研究,项目编号:2021XJYB032,项目名称:基于知识点覆盖率的试题推荐算法研究,项目编号:2021XJYB033。

摘　　要：针对传统单词记忆软件因缺乏语境与单词关联造成的学习效率低下问题,文章采用Pytorch深度学习框架,基于神经网络构建词嵌入模型进行单词联想记忆系统设计。在待学习的英语语料上训练模型并生成词嵌入向量,计算英文单词之间的相似度,通过相似度进行单词关联分析。使用SpringBoot构建后端系统,使用VUE技术构建前端页面,设计并实现单词联想记忆系统。系统依据学习进度,将关联单词及其相应语境图片进行可视化展示,达到单词联想记忆的效果,从而提升单词的记忆效率。To address the inefficiency of traditional word learning software due to the lack of context and word association,the Pytorch deep learning framework is used to build a word embedding model based on neural networks,train the model on the English corpus to be learned and generate a word embedding vector,use the word embedding vector to calculate the similarity between English words,and perform word association analysis through the similarity.The back-end system is built using SpringBoot and the front-end page is built using VUE technology to design and implement the word association memory system.The system visualizes the associated words and their corresponding contextual pictures according to the learning progress,so as to achieve the effect of word association memory and thus improve the efficiency of word memory.

关 键 词：词嵌入模型 神经网络 联想记忆 单词学习 Spring Boot VUE 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]