Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective

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. Base...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2017-12, Vol.29 (12), p.2655-2668
Main Authors: Jie Bai, Linjing Li, Zeng, Daniel, Qiudan Li
Format: Article
Language:English
Subjects:
Activation
association rules
Cognition
Cognitive psychology
cognitive simulation
Data mining
Data models
Enrichment
Knowledge based systems
knowledge representation
Performance enhancement
Psychology
Representations
Semantics
Text analysis
Text mining
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2745565