基于朴素贝叶斯方法的英语缩略词汇推荐模型  

English abbreviation recommendation model based on Naive Bayesian method

作　　者：王渭刚[1] WANG Wei-gang(Institute of Foreign Languages,Weinan Normal University,Weinan 714099,Shaanxi Province,China)

机构地区：[1]渭南师范学院外国语学院,陕西渭南714099

出　　处：《信息技术》2023年第4期18-22,28,共6页Information Technology

基　　金：渭南师范学院2015年教育科学研究项目(2015JKX005);陕西省教育学会2020年度重点课题(SJHZDKT202047)。

摘　　要：为了有效提升英语缩略词汇在网页搜索中的词汇推荐效率与质量,提出基于朴素贝叶斯方法的英语缩略词汇推荐模型。采用特征矢量模型选择英语词汇特征,推算词汇权重,构建朴素贝叶斯分类器,剔除大部分不符合搜索内容的英文词汇;挑选重要内容作为最优推荐主题概念,使用切词手段获得若干英语短语集合,计算网页和关键词相似度,完成英语缩略词汇推荐模型构建。仿真结果证明,所建模型拥有优质的英语缩略词汇推荐质量,推送速度快,为用户提供更加方便快捷的英语搜索服务。In order to effectively improve the efficiency and quality of English abbreviation vocabulary recommendation in web search,this paper proposes a vocabulary recommendation model of English abbreviation based on Naive Bayes method.This paper uses the feature vector model to select the features of English words,calculates the weight of words,constructs a naive Bayesian classifier,and removes most of the English words that do not meet the search content.Then,the important content is selected as the optimal recommendation topic concept,and the word segmentation method is used to obtain a number of English phrase sets.Finally,the similarity of web pages and keywords are calculated to complet the construction of English abbreviation vocabulary recommendation model.The simulation results show that the model has high quality of English abbreviation vocabulary recommendation,fast push speed,and provides more convenient and fast English search service for users.

关 键 词：朴素贝叶斯 英语缩略词汇 推荐模型 概念抽取 最优推荐 

分 类 号：TP397[自动化与计算机技术—计算机应用技术]