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Syntactic Tree Kernels for Event-Time Temporal Relation Learning

  • Seyed Abolghasem Mirroshandel
  • Mahdy Khayyamian
  • Gholamreza Ghassem-Sani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6562)

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.

Keywords

Temporal Relations between Event and Time Information Retrieval Text Mining Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Seyed Abolghasem Mirroshandel
    • 1
  • Mahdy Khayyamian
    • 1
  • Gholamreza Ghassem-Sani
    • 1
  1. 1.Department of Computer EngineeringSharif University of TechnologyTehranIran

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