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Temporal relations learning with a bootstrapped cross-document classifier

Mirroshandel, S. A ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.3233/978-1-60750-606-5-829
  3. Publisher: IOS Press , 2010
  4. Abstract:
  5. The ability to accurately classify temporal relation between events is an important task for a large number of natural language processing applications such as Question Answering (QA), Summarization, and Information Extraction. This paper presents a weakly-supervised machine learning approach for classification of temporal relation between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, it applies the hypothesis of "one type of temporal relation per discourse" and expands the scope of "discourse" from a single document to a cluster of topically-related documents. By combining the global information of such a cluster with local decisions of a general classifier, we propose a novel bootstrapping cross-document classifier to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is at least 7% higher than that of the pattern based state of the art system
  6. Keywords:
  7. Artificial intelligence ; Learning algorithms ; Learning systems ; Natural language processing systems ; Supervised learning ; Cross documents ; Global informations ; Local decisions ; Natural language processing applications ; Question Answering ; State-of-the-art system ; Supervised machine learning ; Temporal relation ; Classification (of information)
  8. Source: Frontiers in Artificial Intelligence and Applications ; Volume 215 , 2010 , Pages 829-834 ; 09226389 (ISSN) ; 9781607506058 (ISBN)
  9. URL: http://ebooks.iospress.nl/publication/5888