In this work we study some probabilistic models for the random generation of words over a given alphabet used in the literature in connection with pattern statistics. Our goal is t...
It is difficult to adapt discriminative classifiers, particularly kernel based ones such as support vector machines (SVMs), to handle mismatches between the training and test da...
We develop the syntactic topic model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, w...
In this paper, we present a method for modeling joint information when generating n-best lists. We apply the method to a novel task of characterizing the similarity of a group of ...
In this paper, we revisit the enumeration of directed animals using gas models. We show that there exists a natural construction of random directed animals on any directed graph t...