2009
NeurIPS
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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Topic Pioneer
- Unsupervised Learning
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
📈
Trend Setter
- Unsupervised Learning
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Keyword Pioneer
- event coreference resolution
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Hot Topic Early Bird
- hierarchical model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Natural Language Processing > Understanding > Coreference Resolution
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Learning Paradigms > Unsupervised Learning
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian