id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_4g77lat4ofeppkced2gc7b6pjq	Silviu Paun	Comparing Bayesian Models of Annotation	2018	15	.pdf	application/pdf	8419	979	59	Comparing Bayesian Models of Annotation comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four literature comparing models of annotation is limited, focused exclusively on synthetic data (Quoc Our findings indicate that models which include annotator structure generally outperform All Bayesian models of annotation that we describe are generative: They provide a mechanism This model pools all annotators (i.e., assumes they have the same ability; see (2008)—are widely used in the literature on annotation models (Carpenter, 2008; Hovy et al., a small number of items and annotators (statistics in Table 1), the different model complexities result in no gains, all the models performing The unpooled models (D&S and MACE) assume each annotator has their own response parameter. this paper further implies that there are no differences between applying these models to corpus annotation or other crowdsourcing tasks. Multilevel Bayesian models of categorical data annotation.	./cache/work_4g77lat4ofeppkced2gc7b6pjq.pdf	./txt/work_4g77lat4ofeppkced2gc7b6pjq.txt