Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Error analysis of the TempEval temporal relation identification task
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
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
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
Automatic system for identifying and categorizing temporal relations in natural language
International Journal of Intelligent Systems
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
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
Temporally anchored relation extraction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
In this paper, we attempt to use a sequence labeling model with features from dependency parsed tree for temporal relation identification. In the sequence labeling model, the relations of contextual pairs can be used as features for relation identification of the current pair. Head-modifier relations between pairs of words within one sentence can be also used as the features. In our preliminary experiments, these features are effective for the temporal relation identification tasks.