Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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
From Italian text to TimeML document via dependency parsing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Identifying event: sentiment association using lexical equivalence and co-reference approaches
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
Automatic system for identifying and categorizing temporal relations in natural language
International Journal of Intelligent Systems
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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Temporal information extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). In this paper, we report our works on TempEval-2 shared task. This is our first participation and we participated in all the tasks, i. e., A, B, C, D, E and F. We develop rule-based systems for Tasks A and B, whereas the remaining tasks are based on a machine learning approach, namely Conditional Random Field (CRF). All our systems are still in their development stages, and we report the very initial results. Evaluation results on the shared task English datasets yield the precision, recall and F-measure values of 55%, 17% and 26%, respectively for Task A and 48%, 56% and 52%, respectively for Task B (event recognition). The rest of tasks, namely C, D, E and F were evaluated with a relatively simpler metric: the number of correct answers divided by the number of answers. Experiments on the English datasets yield the accuracies of 63%, 80%, 56% and 56% for tasks C, D, E and F, respectively.