In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequenc...
Ryan T. McDonald, Kerry Hannan, Tyler Neylon, Mike...
This paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how p...
Terry Koo, Amir Globerson, Xavier Carreras, Michae...
The goal of our work is to improve the Natural Language feedback provided by Intelligent Tutoring Systems. In this paper, we discuss how to make the content presented by one such ...
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Large-scale knowledge-based machine translation requires significant amounts of lexical knowledge in order to map syntactic structures to conceptual structures. This paper present...