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Using syntactic-based kernels for classifying temporal relations

Mirroshandel, S. A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s11390-011-9416-7
  3. Abstract:
  4. 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
  5. Keywords:
  6. Information retrieval ; Kernel function ; Support vector machines ; Temporal relation classification ; Text mining ; Automatically generated ; Improved algorithm ; Natural language processing ; Question answering ; State-of-the-art methods ; Syntactic features ; Temporal relation ; Computational linguistics ; Data mining ; Natural language processing systems ; Syntactics ; Text processing ; Information retrieval
  7. Source: Journal of Computer Science and Technology ; Volume 26, Issue 1 , 2010 , Pages 68-80 ; 10009000 (ISSN)
  8. URL: http://link.springer.com/article/10.1007%2Fs11390-011-9416-7