文献类型：期刊文献

英文题名：Cognitive intentionality extraction from discourse with pragmatic-tree construction and analysis

作者：Guo, Yi[1];Li, Yan[2];Shao, Zhiqing[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Foreign Languages, Shanghai 200237, Peoples R China

年份：2012

卷号：214

起止页码：35

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20123015272329);WOS:【SCI-EXPANDED(收录号:WOS:000307133000003)】；

基金：This work is financially supported by National Natural Science Foundation of China (Grant No. 61003126), the Fundamental Research Funds for the Central Universities (Grant No. WH1114029) and Shanghai Municipal Natural Science Foundation (Grant No. 09ZR1408400) granted to Yi Guo.

语种：英文

外文关键词：Cognitive; Intentionality extraction; P-Tree; Ant colony system; Thematization

摘要：In the research trend of moving towards a fine-grained analysis of subjective and cognitive information, another interdisciplinary and promising research direction in text analysis, intentionality extraction, has recently attracted research interest. Intentionality denotes the process through which humans conceive future situations, plan actions, predict the sensory consequences of the action, and update the prediction with self-changing means. Intentionality extraction is fundamental to the human ability to understand both the general laws that govern events and the particular principle of how and why a specific event actually occurred. As intentionality is a cognitive concept defined as the directedness of the mind towards a content or object, no previous research effort clearly defines and extracts intentionality in discourse. This paper begins by analysing discourse and cognitive intentionality and constructs the CIES-PT system for intentionality extraction based on the P-Tree model (a working model to analyse discourse qua sensible behaviour). CIES-PT applies an ant colony system to cluster similar discourse P-nodes to ensure high cohesion and hierarchically aggregates discourse P-nodes within one cluster to guarantee high coherence. In the final step, CIES-PT identifies intention connections among sentences at discourse levels based on the thematization principle. CIES-PT is examined with elaborately designed experimental tasks using the Reuters-21578 database with three classic metrics (Precision, Recall and F-Measure) and average computing time. The experimental results have demonstrated CIES-PT correctness and effectiveness. (c) 2012 Elsevier Inc. All rights reserved.