Maintaining knowledge about temporal intervals
Communications of the ACM
Machine learning of temporal relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automating temporal annotation with TARSQI
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Experiments with reasoning for temporal relations between events
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Jointly combining implicit constraints improves temporal ordering
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Temporal relation classification task has issues of fourteen target relations, skewed distribution of the target relations, and relatively small amount of data. To overcome the issues, methods such as merging target relations and increasing data size with closure algorithm have been used. However, the method using merged relations has a problem on how to recover original relations. In this paper, a new reduced-relation method is proposed. The method decomposes a target relation into four pairs of endpoints with three target relations. After classifying a relation of each end-point pair, four classified relations are combined into a relation of original fourteen target relations. In the combining step, two heuristics are examined.