Notwithstanding the popularity of machine learning in natural language processing (NLP), rule-based systems have their advantages: distinctive transparency, ease of incorporating external knowledge, and demanding fewer annotations. However, processing efficiency and rule complexity remain major obstacles for adopting rule-based NLP solutions in large clinical data analyses. This talk will introduce a new rule processing engine that allows fast rule execution and structured rule construction for clinical NLP that helps to reduce the rule complexity.
After participating in this activity, the learner should be better able to:
- Understand how trie-based structure can speed up rule processing
- Discuss why exhaustive search is important for accurate rule processing
- Learn how n-trie supports wildcards to simplify rule definitions
- Explain how to use n-trie based NLP components
Jianlin Shi, PhD
Department of Biomedical Informatics
University of Utah
Dr. Jianlin Shi is a research associate of the Department of Biomedical Informatics at the University of Utah. He earned his MD degree from the West China Medical Center of Sichuan University. After a few years of clinical practice, he decided to pursue his career in clinical informatics. He recently finished his PhD training at the University of Utah. His research interests include a broad range of applications using natural language processing to support clinical practice and research.