Rohit J. Kate, Ph.D.
Health Informatics & Administration
- Ph.D., Computer Science, The University of Texas at Austin, 2007
- M.S., Computer Science, The University of Texas at Austin, 2002
- B.Tech., Computer Science and Engineering, Indian Institute of Technology, New Delhi, 2000
- Biomedical Informatics
- Natural Language Processing
- Machine Learning
- Artificial Intelligence
Dr. Kate’s research interests are in natural language processing, biomedical informatics and machine learning. He is interested in applying machine learning techniques to medical applications as well as in developing techniques that would enable computers to robustly understand and process natural languages, especially for biomedical applications. Kate’s research focus is on improving techniques for extracting computer-processable knowledge from natural language texts and on developing techniques for enabling natural language interaction with computers. He has worked on semantic parsing, various forms of supervisions for semantic parser learners, information extraction and kernel-based machine learning methods for natural language processing.
Kate, R. J. (2013). Towards Converting Clinical Phrases into SNOMED CT Expressions. Biomedical Informatics Insights, 6 (Suppl. 1) 29-37.
Kate, R. J. (2013, March). Towards Converting Clinical Phrases into SNOMED CT Expressions. Proceedings of the International Conference on Computational Semantics (IWCS) 2013 Workshop on Computational Semantics in Clinical Text (CSCT) (pp. 34-43). Potsdam, Germany.
Kate, R. J. (2012). Unsupervised Grammar Induction of Clinical Report Sublanguage. Journal of Biomedical Semantics, 3(Suppl 3):S4
Kate, R. J. (2012, January). Optimal Code Length Based Cost for Unsupervised Grammar Induction. Invited Paper. In Proceedings of the National Conference on Innovative Trends in Technology Developments (TECHNOCON 2012) (pp. 68-76). Wardha, India.
Kate, R. J. (2011, December). Unsupervised Grammar Induction of Clinical Report Sublanguage. Proceedings of the International Conference on Machine Learning and Applications, special session on Machine Learning for Biomedical Literature Analysis and Text Retrieval (pp. 53-58). Honolulu, Hawaii.
Blythe, J., Hobbs, J. R., Domingos, P., Kate, R. J., Mooney, R. J. (2011, January). Implementing Weighted Abduction in Markov Logic. Proceedings of the International Conference on Computational Semantics (pp. 55-64). Oxford, England.
Kate, R. J., Luo, X., Patwardhan, S., Franz, M., Florian, R., Mooney, R. J., Roukos, S., & Welty, C. (2010, August). Learning to Predict Readability using Diverse Linguistic Features. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) (pp. 546-554). Beijing, China.
Kate, R. J., & Mooney, R. J. (2010, July). Joint Entity and Relation Extraction using Card-Pyramid Parsing. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010) (pp. 203-212). Uppsala, Sweden.
Kate, R. J., Mooney, R. J. (2009, July). Probabilistic Abduction using Markov Logic Networks. In Proceedings of IJCAI 2009 Workshop on Plan, Activity, and Intent Recognition (PAIR) (pp. 22-28). Pasadena, CA.
Kate, R. J. (2008, October). A Dependency-based Word Subsequence Kernel. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008) (pp. 400-409). Waikiki, Honolulu, Hawaii.
Kate, R. J. (2008, August). Transforming Meaning Representation Grammars to Improve Semantic Parsing. In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL 2008) (pp. 33-40). Manchester, UK.
Kate, R. J., & Mooney, R. J. (2007, July). Learning Language Semantics form Ambiguous Supervision. In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI 2007) (pp. 895-900). Vancouver, Canada.
Kate, R. J., & Mooney, R. J. (2007, April). Semi-Supervised Learning for Semantic Parsing using Support Vector Machines. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (NAACL/HLT-2007) (pp. 81-84 short paper). Rochester, NY.
- Computational Intelligence in Biomedical & Health Care Informatics
- Essential Programming for Biomedical and Health Informatics
- Language Technologies in Biomedicine
- Introduction to Healthcare Informatics
- Artificial Intelligence in Medicine
- Natural Language Processing