A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Timelines from Text: Identification of Syntactic Temporal Relations
ICSC '07 Proceedings of the International Conference on Semantic Computing
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 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Using a Bigram Event Model to Predict Causal Potential
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Cross-document temporal and spatial person tracking system demonstration
NAACL-Demonstrations '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
Temporal Relations Learning with a Bootstrapped Cross-document Classifier
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Emotion cause detection with linguistic constructions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Challenges from information extraction to information fusion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Using syntactic-based kernels for classifying temporal relations
Journal of Computer Science and Technology - Special issue on natural language processing
Unsupervised learning of semantic relation composition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A model for composing semantic relations
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Knowledge and reasoning for question answering: Research perspectives
Information Processing and Management: an International Journal
UTDHLT: COPACETIC system for choosing plausible alternatives
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
Composition of semantic relations: Theoretical framework and case study
ACM Transactions on Speech and Language Processing (TSLP)
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Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. Analysis of these models suggests that additional data will improve performance, and that temporal information is crucial to causal relation identification.