We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
: Selecting an appropriate programming paradigm in which to teach the first programming and problem solving course in a Computer Science undergraduate program has been discussed ex...
The huge volumes of unstructured texts available online drives the increasing need for automated techniques to analyze and extract knowledge from these repositories of information...
The Semantic Web initiative defines important challenges for knowledge representation and database systems. Recently, several standards for representation languages have been pro...
Schema learning is a way to discover probabilistic, constructivist, predictive action models (schemas) from experience. It includes methods for finding and using hidden state to m...