Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
From Language to Time: A Temporal Expression Anchorer
TIME '06 Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
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
What's the date?: high accuracy interpretation of weekday names
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
LCC-TE: a hybrid approach to temporal relation identification in news text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
NAIST.Japan: temporal relation identification using dependency parsed tree
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
USFD: preliminary exploration of features and classifiers for the TempEval-2007 tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2)
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Jointly identifying temporal relations with Markov Logic
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Information Processing and Management: an International Journal
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
Towards generating a patient's timeline: Extracting temporal relationships from clinical notes
Journal of Biomedical Informatics
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We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and anchors temporal expressions, and a maximum entropy classifier assigns temporal link labels, based on features that include descriptions of associated temporal signal words. USFD2 identified temporal expressions successfully, and correctly classified their type in 90% of cases. Determining the relation between an event and time expression in the same sentence was performed at 63% accuracy, the second highest score in this part of the challenge.