Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
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
Error analysis of the TempEval temporal relation identification task
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Temporal information processing of a new language: fast porting with minimal resources
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
USFD2: Annotating temporal expresions and TLINKs for TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Automatic system for identifying and categorizing temporal relations in natural language
International Journal of Intelligent Systems
Aspectual type and temporal relation classification
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
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We describe the Sheffield system used in TempEval-2007. Our system takes a machine-learning (ML) based approach, treating temporal relation assignment as a simple classification task and using features easily derived from the TempEval data, i.e. which do not require 'deeper' NLP analysis. We aimed to explore three questions: (1) How well would a 'lite' approach of this kind perform? (2) Which features contribute positively to system performance? (3) Which ML algorithm is better suited for the TempEval tasks? We used the Weka ML workbench to facilitate experimenting with different ML algorithms. The paper describes our system and supplies preliminary answers to the above questions.