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ACL
2007

Unsupervised Coreference Resolution in a Nonparametric Bayesian Model

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Unsupervised Coreference Resolution in a Nonparametric Bayesian Model
We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing each mention from a combination of global entity properties and local attentional state. Despite being unsupervised, our system achieves a 70.3 MUC F1 measure on the MUC-6 test set, broadly in the range of some recent supervised results.
Aria Haghighi, Dan Klein
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ACL
Authors Aria Haghighi, Dan Klein
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