Bio

Huan Sun is a Ph.D. candidate in the Department of Computer Science at the University of California, Santa Barbara, and is expected to graduate in September 2015. Her research interests lie in data mining and machine learning, with emphasis on text mining, network analysis and human behavior understanding. Particularly, she has been investigating how to model and combine machine and human intelligence for question answering and knowledge discovery. Prior to UCSB, Huan received her B.S. in EE from the University of Science and Technology of China in 2010. She received the UC Regents’ Special Fellowship and the CS Ph.D. Progress Award in 2014. She did summer internships at Microsoft Research and IBM T.J. Watson Research Center. Huan will join the Department of Computer Science at the Ohio State University as an assistant professor in July 2016.

Intelligent and Collaborative Question Answering

Intelligent and Collaborative Question Answering

The paradigm of information search is undergoing a significant transformation with the popularity of mobile devices. Unlike traditional search engines retrieving numerous webpages, techniques that can precisely and directly answer user questions are becoming more desired. We investigate two strategies: (1) Machine intelligent query resolution, where we present two novel frameworks: (i) Schema-less knowledge graph querying. This framework directly searches knowledge bases to answer user queries. It successfully deals with the challenge that answers to user queries could not be simply retrieved by exact keyword and graph matching, due to different information representations. (ii) Combining knowledge bases with the Web. We recognized that knowledge bases are usually far from complete and information required to answer questions may not always exist in knowledge bases. This framework mines answers directly from large-scale web resources, and meanwhile employs knowledge bases as a significant auxiliary to boost question answering performance; (2) Human collaborative query resolution. We made the first attempt to quantitatively analyze expert routing behaviors, i.e., how an expert decides where to transfer a question when she could not solve it. A computational routing model was then developed to optimize team formation and team communication for more efficient problem solving. Future directions of my research include leveraging both machines and humans for better question answering and decision making in various domains such as healthcare and business intelligence.