Abstract
Language understanding is hard because it requires a lot of knowledge. However, the only cost-effective way to acquire a lot of knowledge is by extracting it from text. The best (only?) hope for solving this “chicken and egg” problem is bootstrapping: start with a small knowledge base, use it to process some text, add the extracted knowledge to the KB, process more text, etc. Doing this requires a modeling language that can incorporate noisy knowledge and seamlessly combine it with statistical NLP algorithms. Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov random fields. In this talk, I will describe some of the main inference and learning algorithms for Markov logic, and the progress we have made so far in applying them to NLP. For example, we have developed a system for unsupervised coreference resolution that outperforms state-of-the-art supervised ones on MUC and ACE benchmarks.
Biography
Pedro Domingos is Associate Professor of Computer Science and Engineering at the University of Washington. His research interests are in artificial intelligence, machine learning and data mining. He received a PhD in Information and Computer Science from the University of California at Irvine, and is the author or co-author of over 150 technical publications. He is a member of the advisory board of JAIR, a member of the editorial board of the Machine Learning journal, and a co-founder of the International Machine Learning Society. He was program co-chair of KDD-2003, and has served on numerous program committees. He has received several awards, including a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, and best paper awards at KDD-98, KDD-99 and PKDD-2005.