2019
ACL
ACL 2019
Open Domain Event Extraction Using Neural Latent Variable Models
Abstract
AbstractWe consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— open domain
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Hot Topic Early Bird
— event extraction
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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
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Models > Generative Models
Natural Language Processing > Applications > Information Extraction
Deep Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Paradigms > Unsupervised Learning
Deep Learning > Learning Types > Unsupervised Learning