Multilevel bayesian models of categorical data annotation. Available at http://lingpipe-blog.com/ lingpipe-white-papers
(2008)Abstract
This paper demonstrates the utility of multilevel Bayesian models of data annotation for classifiers (also known as coding or rating). The observable data is the set of categorizations of items by annotators (also known as raters or coders) from which data may be missing at random or may be replicated (that is, it handles fixed panel and varying panel designs). Estimated model parameters