Contact
CSE 534
lsz
cs.washington.edu
cs.washington.eduLanguage and Vision
Online data, including the web and social media, presents an unprecedented visual and textual summary of human lives, events, and activities. We --- the UW NLP and Computer Vision groups --- are working to design scalable new machine learning algorithms that can make sense of this ever growing body of information, by automatically inferring the latent semantic correspondences between language, images, videos, and 3D models of the world.
Interactive Learning for Semantic Parsing
Semantic parsers map natural language sentences to rich, logical representations of their underlying meaning. However, to date, they have been developed primarily for natural language database query applications using learning algorithms that require carefully annotated training data. This project aims to learn robust semantic parsers for a number of new application domains, including robotics interfaces and other spoken dialog systems, with little or no manually annotated training data.
Machine Reading
We seek to apply natural language, information extraction and machine learning methods to build semantic representations of individual texts and large corpora such as the WWW.