A computational model of the semantics of tense and aspect
Computational Linguistics - Special issue on tense and aspect
Computational Linguistics - Special issue on tense and aspect
A maximum entropy approach to natural language processing
Computational Linguistics
Maintaining knowledge about temporal intervals
Communications of the ACM
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Applying machine learning to Chinese temporal relation resolution
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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
Timelines from Text: Identification of Syntactic Temporal Relations
ICSC '07 Proceedings of the International Conference on Semantic Computing
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
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Evaluating temporal graphs built from texts via transitive reduction
Journal of Artificial Intelligence Research
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
This paper investigates the impact of using different temporal algebras for learning temporal relations between events. Specifically, we compare three interval-based algebras: Allen (1983) algebra, Bruce (1972) algebra, and the algebra derived from the TempEval-07 campaign. These algebras encode different granularities of relations and have different inferential properties. They in turn behave differently when used to enforce global consistency constraints on the building of a temporal representation. Through various experiments on the TimeBank/AQUAINT corpus, we show that although the TempEval relation set leads to the best classification accuracy performance, it is too vague to be used for enforcing consistency. By contrast, the other two relation sets are similarly harder to learn, but more useful when global consistency is important. Overall, the Bruce algebra is shown to give the best compromise between learnability and expressive power.