Learning semantic links from a corpus of parallel temporal and causal relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Jointly combining implicit constraints improves temporal ordering
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Comparison of different algebras for inducing the temporal structure of texts
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Using syntactic-based kernels for classifying temporal relations
Journal of Computer Science and Technology - Special issue on natural language processing
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Evaluating temporal graphs built from texts via transitive reduction
Journal of Artificial Intelligence Research
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
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We propose and evaluate a linguistically motivated approach to extracting temporal structure necessary to build a timeline. We considered pairs of events in a verb-clause construction, where the first event is a verb and the second event is the head of a clausal argument to that verb. We selected all pairs of events in the TimeBank that participated in verb-clause constructions and annotated them with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, we were able to train a support vector machine (SVM) model which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of timeline structures from text.