A Top-Down Binary Hierarchical Topic Model for Biomedical Literature

doi:10.1109/ACCESS.2020.2983265

	A Top-Down Binary Hierarchical Topic Model for Biomedical Literature
	Lin, Xiaoguang 1,2,3; Liu, Mingxuan 1,2; Zhang, Ju 1,2
刊名	IEEE ACCESS
	2020
卷号	8 页码:59870-59882
关键词	Topic model topic hierarchy binary modality biomedical literature text mining
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.2983265
通讯作者	Zhang, Ju(zhangju@cigit.ac.cn)
英文摘要	Over the past two decades, a number of advances in topic modeling have produced sophisticated models that are capable of generating topic hierarchies. In particular, hierarchical Latent Dirichlet Allocation (hLDA) builds a topic tree based on the nested Chinese Restaurant Process (nCRP) or other sampling processes to generate a topic hierarchy that allows arbitrarily large branch structures and adaptive dataset growth. In addition, hierarchical topic models based on the latent tree model, such as Hierarchical Latent Tree Analysis (HLTA), have been developed over the last five years. However, these models do not work well in cases with millions of documents and hundreds of thousands of terms. In addition, the topic trees generated by these models are always poorly interpretable, and the relationships among topics in different levels are relatively simple. The biomedical literature, including Medline abstracts, has large-scale documents in two major categories: biological laboratory research and medical clinical research. We propose a top-down binary hierarchical topic model (biHTM) for biomedical literature by iteratively applying a flat topic model and adaptively processing subtrees of the hierarchy. The biHTM topic hierarchy of complete Medline abstracts with more than 14 topic node levels shows good bimodality and interpretability. Compared to hLDA and HLTA, biHTM shows promising results in experiments assessed in terms of runtime and quality.
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000527413100019
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/10920]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Zhang, Ju
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu 610041, Peoples R China
推荐引用方式 GB/T 7714	Lin, Xiaoguang,Liu, Mingxuan,Zhang, Ju. A Top-Down Binary Hierarchical Topic Model for Biomedical Literature[J]. IEEE ACCESS,2020,8:59870-59882.
APA	Lin, Xiaoguang,Liu, Mingxuan,&Zhang, Ju.(2020).A Top-Down Binary Hierarchical Topic Model for Biomedical Literature.IEEE ACCESS,8,59870-59882.
MLA	Lin, Xiaoguang,et al."A Top-Down Binary Hierarchical Topic Model for Biomedical Literature".IEEE ACCESS 8(2020):59870-59882.