Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Machine Learning
Maintaining knowledge about temporal intervals
Communications of the ACM
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Sub-event based multi-document summarization
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
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
On the value of temporal information in information retrieval
ACM SIGIR Forum
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Identification of event mentions and their semantic class
EMNLP '06 Proceedings of the 2006 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
CU-TMP: temporal relation classification using syntactic and semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
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
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
Enhancing QA systems with complex temporal question processing capabilities
Journal of Artificial Intelligence Research
Annotating, extracting and reasoning about time and events
Proceedings of the 2005 international conference on Annotating, extracting and reasoning about time and events
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
JU_CSE_TEMP: A first step towards evaluating events, time expressions and temporal relations
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
NEAT: news exploration along time
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Nowadays, the automatic processing of digitalized documents is crucial to cope with the increasing amount of information available. This issue is addressed from the natural language processing (NLP) research field. One of the tasks required for many NLP applications is temporal information processing. It involves the automatic extraction and interpretation of temporal expressions, events, and their relations. Specifically, the identification and the categorization of temporal relations are the most complex subtasks yet to solve, judging from the results reported in the latest international evaluation exercise. Temporal relation identification has been addressed by very few approaches, and the current categorization approaches are still not a definitive solution. This paper presents a system that approaches temporal relation identification and categorization. The former is approached with a knowledge-driven strategy and the later with data-driven strategy based on different machine-learning techniques. Our proposal has been empirically evaluated over the currently available English data sets annotated with temporal information (TimeBank and AQUAINT) in a 10-fold cross-validated experiment. The results obtained support that the presented approach achieves a high performance. It improves the baseline F1 by 46% and outperforms the state of the art. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.