This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the r...
Even if a problem solving method and a domain ontology has been identified, there still remains the problem of adding sufficient and consistent domain knowledge to a knowledge proc...
Thorsten Liebig, Dieter Finkenzeller, Marko Luther
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) phenotypes. An EM algorithm for maxim...
Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio
We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) d...
In this paper, we present a structural learning model for joint sentiment classification and aspect analysis of text at various levels of granularity. Our model aims to identify ...