Hierarchical Deep Document Model

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yi Yang;John P. Lalor;Ahmed Abbasi;Daniel Dajun Zeng
{"title":"Hierarchical Deep Document Model","authors":"Yi Yang;John P. Lalor;Ahmed Abbasi;Daniel Dajun Zeng","doi":"10.1109/TKDE.2024.3487523","DOIUrl":null,"url":null,"abstract":"Topic modeling is a commonly used text analysis tool for discovering latent topics in a text corpus. However, while topics in a text corpus often exhibit a hierarchical structure (e.g., cellphone is a sub-topic of electronics), most topic modeling methods assume a flat topic structure that ignores the hierarchical dependency among topics, or utilize a predefined topic hierarchy. In this work, we present a novel Hierarchical Deep Document Model (HDDM) to learn topic hierarchies using a variational autoencoder framework. We propose a novel objective function, sum of log likelihood, instead of the widely used evidence lower bound, to facilitate the learning of hierarchical latent topic structure. The proposed objective function can directly model and optimize the hierarchical topic-word distributions at all topic levels. We conduct experiments on four real-world text datasets to evaluate the topic modeling capability of the proposed HDDM method compared to state-of-the-art hierarchical topic modeling benchmarks. Experimental results show that HDDM achieves considerable improvement over benchmarks and is capable of learning meaningful topics and topic hierarchies. To further demonstrate the practical utility of HDDM, we apply it to a real-world medical notes dataset for clinical prediction. Experimental results show that HDDM can better summarize topics in medical notes, resulting in more accurate clinical predictions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"351-364"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10737364/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Topic modeling is a commonly used text analysis tool for discovering latent topics in a text corpus. However, while topics in a text corpus often exhibit a hierarchical structure (e.g., cellphone is a sub-topic of electronics), most topic modeling methods assume a flat topic structure that ignores the hierarchical dependency among topics, or utilize a predefined topic hierarchy. In this work, we present a novel Hierarchical Deep Document Model (HDDM) to learn topic hierarchies using a variational autoencoder framework. We propose a novel objective function, sum of log likelihood, instead of the widely used evidence lower bound, to facilitate the learning of hierarchical latent topic structure. The proposed objective function can directly model and optimize the hierarchical topic-word distributions at all topic levels. We conduct experiments on four real-world text datasets to evaluate the topic modeling capability of the proposed HDDM method compared to state-of-the-art hierarchical topic modeling benchmarks. Experimental results show that HDDM achieves considerable improvement over benchmarks and is capable of learning meaningful topics and topic hierarchies. To further demonstrate the practical utility of HDDM, we apply it to a real-world medical notes dataset for clinical prediction. Experimental results show that HDDM can better summarize topics in medical notes, resulting in more accurate clinical predictions.
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信