Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Machine Learning
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
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Jointly combining implicit constraints improves temporal ordering
EMNLP '08 Proceedings of the 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
NAIST.Japan: temporal relation identification using dependency parsed tree
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
WVALI: temporal relation identification by syntactico-semantic analysis
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
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
USFD2: Annotating temporal expresions and TLINKs for TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
NCSU: Modeling temporal relations with Markov logic and lexical ontology
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Jointly modeling WSD and SRL with Markov logic
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Exploring the effectiveness of lexical ontologies for modeling temporal relations with Markov logic
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Knowledge and reasoning for question answering: Research perspectives
Information Processing and Management: an International Journal
Towards a top-down and bottom-up bidirectional approach to joint information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Coupled temporal scoping of relational facts
Proceedings of the fifth ACM international conference on Web search and data mining
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
Combining flat and structured approaches for temporal slot filling or: how much to compress?
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
Extracting narrative timelines as temporal dependency structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Labeling documents with timestamps: learning from their time expressions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Temporally anchored relation extraction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Coupling label propagation and constraints for temporal fact extraction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Joint inference for event timeline construction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Learning constraints for consistent timeline extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Joint learning for coreference resolution with Markov logic
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Acquiring temporal constraints between relations
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
Learning to predict from textual data
Journal of Artificial Intelligence Research
An inference-based model of word meaning in context as a paraphrase distribution
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Journal of Biomedical Informatics
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Recent work on temporal relation identification has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in isolation by event pairwise comparison. However, this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simultaneously. By evaluating our model on the TempEval data we show that this approach leads to about 2% higher accuracy for all three types of relations ---and to the best results for the task when compared to those of other machine learning based systems.