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Meeting Summary

Jinho Choi

Assistant Professor , Emory University

Speaker Bio

Jinho Choi is an assistant professor of the Department of Computer Science, the Institute of Quantitative Theory and Methods, and the Program of Linguistics at Emory University. He obtained B.A. in Computer Science and Mathematics (dual degree) from Coe College in 2002, M.S.E. in Computer and Information Science from the University of Pennsylvania in 2003 (advisor, Mitch Marcus), Ph.D. in Computer Science and Cognitive Science (joint degree) from the University of Colorado Boulder in 2012 (advsior, Martha Palmer), and post-doctoral training at the University of Massachusetts Amherst in 2014 (advisor, Andrew McCallum). He was a full-time lecturer in the Department of Computer Science at the Korea Military Academy from 2004 to 2007 while he served his military duty in South Korea. Dr. Choi is the founder and the director of the Natural Language Processing Research lab at Emory University.

Dr. Choi has been active in the field of Natural Language Processing (NLP). He has presented several state-of-the-art NLP models for structure prediction that automatically derive linguistic patterns from free-text for part-of-speech tagging, named entity recognition, dependency parsing, semantic role labeling, and coreference resolution. All of these models are publicly available through the cloud-based NLP platform called ELIT, the successor of NLP4J and ClearNLP, that Dr. Choi has developed to promote academic and industrial research. Since he came to Emory, Dr. Choi has introduced several machine comprehension tasks to identify personal entities in multiparty dialog and infer explicit as well as implicit contexts such as emotions about individual characters in dialog. In his recent work, Dr. Choi introduced a reading comprehension model that interprets actions or events described by speakers in dialog. For the application of his research, Dr. Choi has developed an innovative model for severity classification on radiology reports using deep neural networks, which showed the classification accuracy as high as trained radiologists.

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