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

作者：Jie Bai, Linjing Li, Daniel Zeng, Qiudan Li · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2017 · DOI：10.1109/tkde.2017.2745565 · 被引用次数：5 · 研究领域：Topic Modeling、Advanced Text Analysis Techniques、Sentiment Analysis and Opinion Mining

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.