Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective
Bai, Jie1,2; Li, Linjing1; Zeng, Daniel1,3; Li, Qiudan1
2017-12-01
发表期刊IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷号29期号:12页码:2655-2668
文章类型Article
摘要In 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.
关键词Text Analysis Knowledge Representation Cognitive Simulation Association Rules
WOS标题词Science & Technology ; Technology
DOI10.1109/TKDE.2017.2745565
关键词[WOS]TEXT REPRESENTATION MODEL ; CLASSIFICATION
收录类别SCI
语种英语
项目资助者National 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研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号WOS:000414712700003
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19855
专题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
作者单位1.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
推荐引用方式
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
tkde-bai-2745565-pro(3183KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Bai, Jie]的文章
[Li, Linjing]的文章
[Zeng, Daniel]的文章
百度学术
百度学术中相似的文章
[Bai, Jie]的文章
[Li, Linjing]的文章
[Zeng, Daniel]的文章
必应学术
必应学术中相似的文章
[Bai, Jie]的文章
[Li, Linjing]的文章
[Zeng, Daniel]的文章
相关权益政策
暂无数据
收藏/分享
文件名: tkde-bai-2745565-proof.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。