Structured Prediction with Indirect Supervision

May 24, 2011
Ming-Wei Chang | University of Illinois at Urbana-Champaign (UIUC)

Machine learning (ML) has already made significant impacts on our daily life. From hand-written digit recognition, spam filtering to ranking search results, machine learning techniques help us build intelligent systems more easily and make computers seem smarter. Nevertheless, current ML techniques support limited set of supervision protocols, making it difficult to transfer human knowledge to machines efficiently without labeling examples explicitly. However, structured tasks, which involve many interdependent decisions for a given example, are expensive to label. Given that many important tasks in natural language processing and information extraction are structured tasks, it is important to develop learning frameworks that can use knowledge resources and other sources of indirect supervision in addition to labeled examples for the current task.

I will present my work on reducing the labeling cost of structured tasks using indirect supervision protocols and knowledge based constraints. We have developed advanced machine learning algorithms that take advantage of indirect supervision along with existing labeled data. Indirect supervision can come in the form of constraints or of weaker, easy to get, supervision signals. Our proposed learning frameworks can handle both structured output problems and problems with latent structures. We demonstrate the effectiveness of our indirect supervision frameworks on several important natural language processing tasks.

Speaker Details

Ming-Wei Chang is a Ph.D. candidate in University of Illinois at Urbana-Champaign. His research interest spans machine learning and its application to natural language processing and information retrieval.

Ming-Wei has published several papers in leading machine learning and natural language processing conferences, including ICML, ACL, EMNLP, NAACL, KDD and AAAI. In 2009 and 2010, he has co-presented tutorials on combining human knowledge with statistical models in EACL and NAACL, respectively. Before becoming a Ph.D. student, Ming-Wei has worked on several support vector machine projects with Professor Chih-Jen Lin in National Taiwan University, and has won the first place in two international machine learning competitions.