Associated Activation-Driven Enrichment Understanding Implicit Information from a Cognitive Perspective
Associated Activation-Driven Enrichment Understanding Implicit Information from a Cognitive Perspective
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关联激活驱动的丰富：从认知角度理解隐式信息
DOI：
10.1109/tkde.2017.2745565
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发表时间：
2017
影响因子：
8.9
通讯作者：
Qiudan Li
中科院分区：
文献类型：
--
作者：
Jie Bai;Linjing Li;Daniel Zeng;Qiudan Li
在本文中，我们提出了一种新颖的文本表示范式和一套基于认知心理学理论的后续文本表示模型。我们研究的直觉是，大量文档中隐含的知识可能会提高对单个文档的理解。基于认知心理学理论，我们提出了一个通用的文本丰富框架，研究了激活隐性信息的关键因素，并开发了新的文本表示方法来丰富文本的隐性信息。我们的研究旨在模仿人类认知过程的某些方面，其中给定的刺激性单词可以激活对隐含概念的理解。通过将人类认知融入文本表示中，所提出的模型通过从给定文本中挖掘隐式信息并同时与大多数现有文本表示方法相协调来推进现有研究，这从本质上弥合了显式信息和隐式信息之间的差距。多个任务的实验表明，我们提出的模型激活的隐式信息符合人类直觉，并显着提高了文本挖掘任务的性能。
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.