@InProceedings{huang-EtAl:2017:SemEval,
  author    = {Huang, Po-Yu  and  Huang, Hen-Hsen  and  Wang, Yu-Wun  and  Huang, Ching  and  Chen, Hsin-Hsi},
  title     = {NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1010--1013},
  abstract  = {This study proposes a system to participate in the Clinical TempEval 2017
	shared task, a part of the SemEval 2017 Tasks. Domain adaptation was the main
	challenge this year. We took part in the supervised domain adaption where data
	of 591 records of colon cancer patients and 30 records of brain cancer patients
	from Mayo clinic were given and we are asked to analyze the records from brain
	cancer patients. Based on the THYME corpus released by the organizer of
	Clinical TempEval, we propose a framework that automatically analyzes clinical
	temporal events in a fine-grained level. Support vector machine (SVM) and
	conditional random field (CRF) were implemented in our system for different
	subtasks, including detecting clinical relevant events and time expression,
	determining their attributes, and identifying their relations with each other
	within the document. The results showed the capability of domain
	adaptation of our system.},
  url       = {http://www.aclweb.org/anthology/S17-2177}
}