2018
NAACL
NAACL 2018
Determining Event Durations: Models and Error Analysis
Abstract
AbstractThis paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Keyword Pioneer
— event duration prediction
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Hot Topic Early Bird
— temporal modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio