Faculty • Affiliated Faculty • Research Associates • Graduate Students

Raymond Mooney

Office:	Taylor 4.130B
Email:	mooney@cs.utexas.edu
Homepage:	cs.utexas.edu/users/mooney/

Research Interests

Machine learning is the study of adaptive computational systems that improve performance with experience. Our research focuses on combining empirical and knowledge-based learning techniques, including applications such as natural language acquisition, recommender systems and text data mining.

Graduate Students

Rupert Tang

Un Yong Nahm

Projects

Natural Language Learning: Natural language processing systems are difficult to build and machine learning methods can help automate their construction significantly. Our research in learning for natural language mainly involves applying inductive logic programming and other relational learning techniques to constructing natural language database interfaces and information extraction systems from supervised examples.

Text Data Mining: Text data mining concerns the application of data mining (knowledge discovery in databases, KDD) to unstructured textual data. Our work focuses on using information extraction to first extract a structured database from a corpus of natural language texts and then discovering interesting patterns, rules or trends in the resulting database using traditional KDD tools.

Learning for Recommender Systems: Recommender systems suggest information sources and products to users based on learning from examples of their likes and dislikes. Unlike collaborative filtering methods that base recommendation on other users' preferences, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. Our work has focused on a content-based book recommending system called LIBRA.

Machine Learning Research Group

Courses

CS 343	Artificial Intelligence
CS 351	LISP and Symbolic Computation
CS 391L	Intelligent Information Retrieval

Selected Publications

Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing

A Mutually Beneficial Integration of Data Mining and Information Extraction

Content-Based Book Recommending Using Learning for Text Categorizaion