监督式主题模型及其应用综述  

Review of Supervised Topic Models and Applications

作　　者：王振彪 徐贞顺 刘纳 张文豪[1,2] 唐增金 王正安 WANG Zhenbiao;XU Zhenshun;LIU Na;ZHANG Wenhao;TANG Zengjin;WANG Zheng’an(College of Compute Science and Engineering,North Minzu University,Yinchuan 750021,China;The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu Uni-versity,Yinchuan 750021,China)

机构地区：[1]北方民族大学计算机科学与工程学院,银川750021 [2]北方民族大学图像图形智能处理国家民委重点实验室,银川750021

出　　处：《计算机工程与应用》2024年第8期56-68,共13页Computer Engineering and Applications

基　　金：宁夏自然科学基金(2021AAC03217,2021AAC03224)。

摘　　要：主题模型是一种数据挖掘的方法,可以自动地从大量文件或数据中提取潜在的模式或主题,并将对应的数据分配到相应的模式或主题中。主题模型已广泛应用于文本聚类或分类、主题抽取、主题演变、情感分析和摘要总结等领域。监督式主题模型和非监督主题模型的区别在于是否依赖标注信息。近年来,监督式主题模型在数据挖掘任务中逐渐兴起,使得越来越多的任务倾向于采用监督式方法进行优化。陈述了监督式主题模型相关内容,介绍常用的数据集和评价指标;分别从模型和应用的角度对各种类型的监督式主题模型进行了深入对比分析。最后,阐述了主题模型当前研究所面临的挑战,并对未来监督式主题模型的研究方向进行展望。Topic model is a data mining method that can automatically extract potential patterns or topics from a large number of files or data,and assign the corresponding data to the corresponding patterns or topics.Topic models have been widely used in the fields of text clustering or classification,topic extraction,topic evolution,sentiment analysis and summary.The difference between a supervised topic model and an unsupervised topic model is whether it relies on annota-tion information.In recent years,supervised topic model has gradually emerged in data mining tasks,which makes more and more tasks tend to adopt supervised method for optimization.Firstly,the content of supervised topic model is presented,and the commonly used data sets and evaluation indicators are introduced.Secondly,from the perspective of model and application,different types of supervised topic models are analyzed in depth.Finally,the challenges facing the current research of thematic models are described,and the future research direction of supervised thematic models is prospected.

关 键 词：数据挖掘 监督式主题模型 主题预测 主题演变 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]