Modeling and Exploiting Review Helpfulness for Summarization
- 14:00 14th May 2014 ( week 3, Trinity Term 2014 )Room 051
This talk will illustrate some of the opportunities and challenges in processing both commercial and educational review corpora with respect to helpfulness. I will first present a content-based approach for automatically predicting review helpfulness, where features representing language usage, content diversity and helpfulness-related topics are selectively extracted from review text. Experimental results across camera, movie, and student peer reviews demonstrate the utility of the approach. I will then present two extractive approaches to review summarization, where helpfulness ratings are used to either guide review-level filtering or to supervise a topic model for sentence-level content scoring. Experimental results show that helpfulness-guided review summarizers can outperform traditional methods in human and automated evaluations.
This research has been performed in collaboration with Wenting Xiong, University of Pittsburgh.
Speaker bio
Diane Litman is Professor of Computer Science, Director of the Graduate Program in Intelligent Systems, and Senior Scientist with the Learning Research and Development Center, all at the University of Pittsburgh. She has been working in the field of artificial intelligence since she received her Ph.D. degree in Computer Science from the University of Rochester. Before joining Pitt, she was a member of the Artificial Intelligence Principles Research Department, AT&T Labs - Research (formerly Bell Laboratories). Dr. Litman's current research focuses on enhancing the effectiveness of educational technology through the use of spoken and natural language processing. Dr. Litman has been Chair of the North American Chapter of the Association for Computational Linguistics, has co-authored multiple papers winning best paper awards, and has been awarded Senior Member status by the Association for the Advancement of Artificial Intelligence.Share this:
