Maintaining knowledge about temporal intervals
Communications of the ACM
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Inferring temporal ordering of events in news
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Automating temporal annotation with TARSQI
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Experiments with reasoning for temporal relations between events
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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
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
Predicting unknown time arguments based on cross-event propagation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
Distant supervision for relation extraction without labeled data
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 2 - Volume 2
Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia
Proceedings of the 13th International Conference on Extending Database Technology
Reasoning about fuzzy temporal information from the web: towards retrieval of historical events
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing on Web; Guest Editors: A. G. López-Herrera, E. Herrera-Viedma
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Harvesting facts from textual web sources by constrained label propagation
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
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graph-based document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.