id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_643a5sia2bchnksdxl2mqbqyka	David Bamman	Unsupervised Discovery of Biographical Structure from Text	2014	14	.pdf	application/pdf	7752	753	65	event classes in biographies, based on a probabilistic latent-variable model. and observational data; we present here a latentvariable model that exploits the correlations of event To analyze the latent event classes in Wikipedia biographies, we train our full model (with a logistic normal prior and time as an observable variable) on the full dataset of 242,970 biographies with The latent classes that we learn span a mix of major life events of Wikipedia notable figures (including events that we might characterize as GRADUATING HIGH SCHOOL, BECOMING A CITIZEN, DIVORCE, BEING CONVICTED OF A CRIME, and DYING) and more fine-grained events (such as BEING DRAFTED BY A SPORTS TEAM and BEING INDUCTED INTO THE HALL OF FAME). exists between event classes, we draw on the original work on procedural scripts and schemas (Minsky, 1974; Schank and Abelson, 1977) and narrative chains (Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009), including more recent advances in the unsupervised learning of frame semantic representations (Modi et al., 2012; O'Connor,	./cache/work_643a5sia2bchnksdxl2mqbqyka.pdf	./txt/work_643a5sia2bchnksdxl2mqbqyka.txt