id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ddz5r4ceerf45fsxq46cai43tu	Bishan Yang	A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution	2015	12	.pdf	application/pdf	7287	632	60	We present a novel hierarchical distancedependent Bayesian model for event coreference resolution. widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and importantly, event coreference resolution is a necessary component in any reasonable, broadly applicable computational model of natural language understanding (Humphreys et al., 1997). the incorporation of feature-based, learnable distance functions as clustering priors, thus encouraging event mentions that are close in meaning to belong to the same cluster. learning of event coreference relations with unsupervised hierarchical modeling of event clustering Coreference resolution in general is a difficult natural language processing (NLP) task and typically requires sophisticated inferentially-based knowledgeintensive models (Kehler, 2002). Bejan and Harabagiu (2010; 2014) proposed several unsupervised generative models for event mention clustering based on the hierarchical Dirichlet Thus it is ideal to have a twolevel clustering model that can group event mentions within a document and further group them	./cache/work_ddz5r4ceerf45fsxq46cai43tu.pdf	./txt/work_ddz5r4ceerf45fsxq46cai43tu.txt