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Journal of Computer Science and Technology 2011, Vol. 26 Issue (1) :68-80    DOI: 10.1007/s11390-011-1112-0
Special Section on Natural Language Processing Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Using Syntactic-Based Kernels for Classifying Temporal Relations
Seyed Abolghasem Mirroshandel, Gholamreza Ghassem-Sani, and Mahdy Khayyamian
Department of Computer Engineering, Sharif University of Technology, Tehran 11155-9517, Iran

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Abstract 

Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that adding syntactic features results in a considerable improvement over the state-of-the-art method of classifying temporal relations.

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Keywordstemporal relation classification   information retrieval   text mining   support vector machines   kernel function     
Received 2009-12-31;
Cite this article:   
Seyed Abolghasem Mirroshandel, Gholamreza Ghassem-Sani, and Mahdy Khayyamian.Using Syntactic-Based Kernels for Classifying Temporal Relations[J]  Journal of Computer Science and Technology, 2011,V26(1): 68-80
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-011-1112-0
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