Automatically Constructing Multi-Dimensional Resource Space by Extracting Class Trees From Texts for Operating and Analyzing Texts From Multiple Abstraction Dimensions

doi:10.1109/ACCESS.2024.3516872

	Automatically Constructing Multi-Dimensional Resource Space by Extracting Class Trees From Texts for Operating and Analyzing Texts From Multiple Abstraction Dimensions
	Zhou, Jian 1,2; Li, Jiazheng 1,2; Zhuge, Sirui 3,4; Zhuge, Hai 5,6
	2025
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	13 页码:4737-4758
摘要	The Abstraction is a key part of understanding and representation. Discovering different abstraction dimensions on a large set of texts can help understand the texts from multiple dimensions therefore support multi-dimensional operations required by advanced applications. This paper proposes a low-cost approach to automatically discovering abstraction dimensions represented as class trees on texts. The approach consists of three steps: 1) extract subclass relations from input texts based on modifier pattern and syntactic pattern; 2) construct class trees based on the extracted subclass relations; and 3) select independent class trees with high coverage on texts as abstraction dimensions. The correctness and feasibility of the approach are validated on seven data sets of different types. The average precision, recall and F1-score of the extracted subclass relations of the proposed approach are all greater than 85%. The application of the proposed approach to managing GitHub projects demonstrates that searching on the class trees ensures strong relevance between query and return, can quickly reduce search space and support effective management of projects. The proposed approach not only greatly extends the pattern-based approach to finding abstraction relation from texts with a high coverage but also verifies the feasibility of automatically extracting abstraction dimensions from texts. It can be applied to efficiently manage large-scale text resources from different dimensions to support advanced applications.
关键词	Abstraction dimension natural language processing pattern resource space subclass relation text management
DOI	10.1109/ACCESS.2024.3516872
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61876048]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001395555100024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40747
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuge, Hai
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Kings Coll London, London WC2R 2LS, England 4.Publ Sapient, London EC1M 5NP, England 5.Great Bay Univ, Dongguan 523000, Peoples R China 6.Great Bay Inst Adv Study, Dongguan 523000, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Jian,Li, Jiazheng,Zhuge, Sirui,et al. Automatically Constructing Multi-Dimensional Resource Space by Extracting Class Trees From Texts for Operating and Analyzing Texts From Multiple Abstraction Dimensions[J]. IEEE ACCESS,2025,13:4737-4758.
APA	Zhou, Jian,Li, Jiazheng,Zhuge, Sirui,&Zhuge, Hai.(2025).Automatically Constructing Multi-Dimensional Resource Space by Extracting Class Trees From Texts for Operating and Analyzing Texts From Multiple Abstraction Dimensions.IEEE ACCESS,13,4737-4758.
MLA	Zhou, Jian,et al."Automatically Constructing Multi-Dimensional Resource Space by Extracting Class Trees From Texts for Operating and Analyzing Texts From Multiple Abstraction Dimensions".IEEE ACCESS 13(2025):4737-4758.