Kai-Wei Chang, Department of Computer Science @ University of Virgina


Many machine learning problems involve making joint predictions over a set of mutually dependent output variables. The dependencies between output variables can be represented by a structure, such as a sequence, a tree, a clustering of nodes, or a graph. Structured prediction models have been proposed for problems of this type, and they have been shown to be successful in many application areas, such as natural language processing, computer vision, and bioinformatics. There are two families of algorithms for these problems: graphical model approaches and learning to search approaches. In this talk, I will describe a collection of results that improve several aspects of these approaches. Our results lead to efficient learning algorithms for structured prediction models and for online clustering models, which, in turn, support reduction in problem size, improvements in training and evaluation speed, and improved performance. We have used our algorithms to learn expressive models from large amounts of annotated data and achieve state-of-the-art performance on several natural language processing tasks.



Software & Demos


Coreference Resolution

Other NLP Applications

Learning and Inference for Structured Prediction

Implicit Supervision

Social Biases in Learning Models