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We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales.
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Semeval-2007 task 15: Tempeval temporal relation identification
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Nathanael Chambers and Dan Jurafsky. 2008 · 2008
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Semeval-2010 task 13: Tempeval-2
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Scikit-learn: Machine Learning in Python
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Extracting and modeling durations for habits and events from twitter
Universal Dependencies 1.2
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Yu Hong, Tongtao Zhang, Tim O’Gorman, Sharone Horowit-Hendler, Heng Ji, and Martha Palmer. 2016 · 2016
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Stacking approach to temporal relation classification with temporal inference
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Steven Bethard. 2013 · 2013
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Jennifer D’Souza and Vincent Ng. 2013 · 2013
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Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations
Naushad UzZaman, Hector Llorens, Leon Derczynski, James Allen, Marc Verhagen, and James Pustejovsky. 2013 · 2013
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An annotation framework for dense event ordering
Taylor Cassidy, Bill McDowell, Nathanael Chambers, and Steven Bethard. 2014 · 2014
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Dense event ordering with a multi-pass architecture
Nathanael Chambers, Taylor Cassidy, Bill McDowell, and Steven Bethard. 2014 · 2014
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Meantime, the newsreader multilingual event and time corpus
Anne-Lyse Myriam Minard, Manuela Speranza, Ruben Urizar, Begona Altuna, Marieke van Erp, Anneleen Schoen, and Chantal van Son. 2016 · 2016
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Catena: Causal and temporal relation extraction from natural language texts
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Richer event description: Integrating event coreference with temporal, causal and bridging annotation
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Universal decompositional semantics on universal dependencies
Aaron Steven White, Drew Reisinger, Keisuke Sakaguchi, Tim Vieira, Sheng Zhang, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2016 · 2016
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Classifying temporal relations by bidirectional lstm over dependency paths
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Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, and Guergana Savova. 2017 · 2017
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Structured learning for temporal relation extraction from clinical records
Tuur Leeuwenberg and Marie-Francine Moens. 2017 · 2017
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A structured learning approach to temporal relation extraction
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Integrating deep linguistic features in factuality prediction over unified datasets
Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, and Iryna Gurevych. 2017 · 2017
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Neural architecture for temporal relation extraction: A bi-lstm approach for detecting narrative containers
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An evaluation of predpatt and open ie via stage 1 semantic role labeling
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Temporal information extraction by predicting relative time-lines
Artuur Leeuwenberg and Marie-Francine Moens. 2018 · 2018
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Joint reasoning for temporal and causal relations
Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme. 2018 · 2018
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Lexicosyntactic inference in neural models
Aaron Steven White, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2018 · 2018
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