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Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events.
Learning schemata for natural language processing
Raymond Mooney and Gerald DeJong. 1985 · 1985
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The Proposition Bank: An annotated corpus of semantic roles
Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005 · 2005
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Machine learning of temporal relations
Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong Min Lee, and James Pustejovsky. 2006 · 2006
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Classifying temporal relations between events
Nathanael Chambers, Shan Wang, and Dan Jurafsky. 2007 · 2007
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SemEval-2007 task 15: TempEval temporal relation identification
Marc Verhagen, Robert Gaizauskas, Frank Schilder, Mark Hepple, Graham Katz, and James Pustejovsky. 2007 · 2007
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Unsupervised learning of narrative event chains
Nathanael Chambers and Dan Jurafsky. 2008 · 2008
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Temporal processing with the TARSQI toolkit
Marc Verhagen and James Pustejovsky. 2008 · 2008
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Narrative interpolation for generating and understanding stories
Su Wang, Greg Durrett, and Katrin Erk. 2020 · 2008
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Eigen: Event influence generation using pre-trained language models
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Aman Madaan and Yiming Yang. 2020 · 2010
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P. Vincent, H. Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
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Temporal reasoning on implicit events from distant supervision
Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, A. Sabharwal, and D. Roth. 2020 · 2010
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Joint inference for event timeline construction
Quang Do, Wei Lu, and Dan Roth. 2012 · 2012
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Skip n-grams and ranking functions for predicting script events
Bram Jans, Steven Bethard, Ivan Vulic, and Marie-Francine Moens. 2012 · 2012
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Event schema induction with a probabilistic entity-driven model
Nathanael Chambers. 2013 · 2013
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Speech enhancement based on deep denoising autoencoder
X. Lu, Y. Tsao, S. Matsuda, and C. Hori. 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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Event embeddings for semantic script modeling
Ashutosh Modi. 2016 · 2016
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016a · 2016
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Haoruo Peng and Dan Roth. 2016 · 2016
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AllenNLP: A Deep Semantic Natural Language Processing Platform
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Story generation from sequence of independent short descriptions
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Tanya Goyal and Greg Durrett. 2019 · 2019
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Joint event and temporal relation extraction with shared representations and structured prediction
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Diversity-aware event prediction based on a conditional variational autoencoder with reconstruction
Hirokazu Kiyomaru, Kazumasa Omura, Yugo Murawaki, Daisuke Kawahara, and Sadao Kurohashi. 2019 · 2019
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017 · 2017
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Structured learning for temporal relation extraction from clinical records
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A joint model for semantic sequences: Frames, entities, sentiments
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Neural architecture for temporal relation extraction: A Bi-LSTM approach for detecting narrative containers
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“going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding
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