2009
NIPS
NeurIPS 2009
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
Abstract
We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. In this approach, we consider a potentially infinite number of features and categorical outcomes. We evaluate these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when compared against an existing baseline for this task.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— event coreference resolution
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Hot Topic Early Bird
— unsupervised learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Trend Setter
— Coreference Resolution
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Understanding > Coreference Resolution
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian