Syntactic tree kernels for event-time temporal relation learning

Authors:
Seyed Abolghasem Mirroshandel;Mahdy Khayyamian;Gholamreza Ghassem-Sani
Affiliations:
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Venue:
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Year:
2009

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Abstract

Temporal relation classification is one of the contemporary demanding tasks in 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 and times, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting useful syntactic features, which are automatically generated, to improve accuracy of the classification. Accordingly, a number of novel kernel functions are introduced and evaluated for temporal relation classification. The result of experiments clearly shows that adding syntactic features results in a notable performance improvement over the state of the art method, which merely employs gold-standard features.