Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective
Bai, Jie1,2; Li, Linjing1; Zeng, Daniel1,3; Li, Qiudan1
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2017-12-01
Volume29Issue:12Pages:2655-2668
SubtypeArticle
AbstractIn this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.
KeywordText Analysis Knowledge Representation Cognitive Simulation Association Rules
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TKDE.2017.2745565
WOS KeywordTEXT REPRESENTATION MODEL ; CLASSIFICATION
Indexed BySCI
Language英语
Funding OrganizationNational Key R&D Program of China(2016QY02D0205) ; National Natural Science Foundation of China(71202169 ; Chinese Academy of Sciences(ZDRW-XH-2017-3) ; SKLMCCS ; 71602184 ; 71621002 ; 61671450 ; U1435221)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000414712700003
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19855
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100049, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
Recommended Citation
GB/T 7714
Bai, Jie,Li, Linjing,Zeng, Daniel,et al. Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(12):2655-2668.
APA Bai, Jie,Li, Linjing,Zeng, Daniel,&Li, Qiudan.(2017).Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(12),2655-2668.
MLA Bai, Jie,et al."Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.12(2017):2655-2668.
Files in This Item: Download All
File Name/Size DocType Version Access License
tkde-bai-2745565-pro(3183KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Bai, Jie]'s Articles
[Li, Linjing]'s Articles
[Zeng, Daniel]'s Articles
Baidu academic
Similar articles in Baidu academic
[Bai, Jie]'s Articles
[Li, Linjing]'s Articles
[Zeng, Daniel]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bai, Jie]'s Articles
[Li, Linjing]'s Articles
[Zeng, Daniel]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: tkde-bai-2745565-proof.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.