Our HDP extension is also inspired from the Bayesian model proposed by Haghighi and Klein ( 2007 ) .
unsupervised coreference resolution in a nonparametric bayesian model
Aria Haghighi Dan Klein
We present an extension of the hierarchical Dirichlet process ( HDP ) model which is able to represent each observable object ( i.e. , event mention ) by a finite number of feature types L . Our HDP extension is also inspired from the Bayesian model proposed by Haghighi and Klein ( 2007 ) . However , their model is strictly customized for entity coreference resolution , and therefore , extending it to include additional features for each observable object is a challenging task ( Ng , 2008 ; Poon and Domingos , 2008 ) .
Our HDP extension is also inspired from the Bayesian model proposed by @@CITATION .