This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often anno...
S. R. K. Branavan, Harr Chen, Jacob Eisenstein, Re...
We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect bia...
We apply robust Bayesian decision theory to improve both generative and discriminative learners under bias in class proportions in labeled training data, when the true class propo...
1 We propose a paradigm for ecologically valid, authentic, unobtrusive, automatic, data-rich, fast, robust, and sensitive evaluation of computer-assisted student performance. We i...
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...