Title: Constrained Conditional Models: Integer Linear Programming Formulations for Natural Language Understanding Speaker: Dan Roth Computer Science and the Beckman Institute University of Illinois at Urbana/Champaign Time: 11am-12:30pm, January 31, Thursday Place: Room 9205/9206, CUNY Graduate Center. 5th Ave & 34th St. Abstract: Computational approaches to problems in Natural Language Understanding and Information Extraction often involve assigning values to sets of interdependent variables. Examples include semantic role labeling (analyzing natural language text at the level of “who did what to whom, when and where”), syntactic parsing, Identifying events, entities and relations in natural language text, transliteration of names, and textual entailment (determining whether one utterance is a likely consequence of another). Over the last few years, one of the most successful approaches to studying these problems involves Constrained Conditional Models (CCMs), an Integer Learning Programming formulation that augments probabilistic models with declarative constraints as a way to support such decisions. I will present research within this framework, discussing old and new results pertaining to inference issues, learning algorithms for training these global models, and the interaction between learning and inference. Short Bio: Dan Roth is a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign and a University of Illinois Scholar. He is the director of a DHS Center for Multimodal Information Access & Synthesis (MIAS) and holds faculty positions in Statistics, Linguistics and at the School of Library and Information Sciences. Roth is a Fellow of the ACM, AAAI and ACL, for his contributions to Machine Learning and to Natural Language Processing. He has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely by the research community. Roth is the Associate Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR) and will serve as Editor-in-Chief for a two-year term beginning in 2015. He was the program chair of AAAI’11, ACL’03 and CoNLL'02, has been on the editorial board of several journals in his research areas and has won several teaching and paper awards. He has also given keynote talks and presented tutorials in some of the major conferences in his research areas. Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D in Computer Science from Harvard University in 1995.