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Syntactic tree kernels for event-time temporal relation learning

Mirroshandel, S. A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-20095-3_20
  3. Abstract:
  4. 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
  5. Keywords:
  6. Automatically generated ; Classification ; Improved algorithm ; Kernel function ; Natural language processing ; Performance improvements ; Question answering ; State-of-the-art methods ; Syntactic features ; Syntactic trees ; Temporal relation ; Temporal relations between event and time ; Text mining ; Computational linguistics ; Data mining ; Information retrieval ; Natural language processing systems ; Support vector machines ; Syntactics ; Text processing ; Classification (of information)
  7. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 6562 LNAI , 2011 , Pages 213-223 ; 03029743 (ISSN) ; 9783642200946 (ISBN)
  8. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-20095-3_20