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An Acquisition Model of Deep Textual Semantics Based on Human Reading Cognitive Process

An Acquisition Model of Deep Textual Semantics Based on Human Reading Cognitive Process

Jun Zhang, Xiangfeng Luo, Lei Lu, Weidong Liu
Copyright: © 2012 |Volume: 6 |Issue: 2 |Pages: 22
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466611184|DOI: 10.4018/jcini.2012040105
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MLA

Zhang, Jun, et al. "An Acquisition Model of Deep Textual Semantics Based on Human Reading Cognitive Process." IJCINI vol.6, no.2 2012: pp.82-103. http://doi.org/10.4018/jcini.2012040105

APA

Zhang, J., Luo, X., Lu, L., & Liu, W. (2012). An Acquisition Model of Deep Textual Semantics Based on Human Reading Cognitive Process. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 6(2), 82-103. http://doi.org/10.4018/jcini.2012040105

Chicago

Zhang, Jun, et al. "An Acquisition Model of Deep Textual Semantics Based on Human Reading Cognitive Process," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 6, no.2: 82-103. http://doi.org/10.4018/jcini.2012040105

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Abstract

The acquisition of deep textual semantics is a key issue which significantly improves the performances of e-learning, web search and web knowledge services, etc. Though many models have been developed to acquire textual semantics, the acquisition of deep textual semantics is still a challenge issue. Herein, an acquisition model of deep textual semantics is developed to enhance the capability of text understanding, which includes two parts: 1) how to obtain and organize the domain knowledge extracted from text set and 2) how to activate the domain knowledge for obtaining the deep textual semantics. The activation process involves the Gough mode reading theory, Landscape model and memory cognitive process. The Gough mode is the main human reading model that enables the authors to acquire deep semantics in a text reading process. Generalized semantic field is proposed to store the domain knowledge in the form of Long Term Memory (LTM). Specialized semantic field, which is acquired by the interaction process between the text fragment and the domain knowledge, is introduced to describe the change process of textual semantics. By their mutual actions, the authors can get the deep textual semantics which enhances the capability of text understanding; therefore, the machine can understand the text more precisely and correctly than those models only obtaining surface textual semantics.

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