@inproceedings{huang-etal-2017-ntu,
title = "{NTU}-1 at {S}em{E}val-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation",
author = "Huang, Po-Yu and
Huang, Hen-Hsen and
Wang, Yu-Wun and
Huang, Ching and
Chen, Hsin-Hsi",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2177",
doi = "10.18653/v1/S17-2177",
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.",
}
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%0 Conference Proceedings
%T NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation
%A Huang, Po-Yu
%A Huang, Hen-Hsen
%A Wang, Yu-Wun
%A Huang, Ching
%A Chen, Hsin-Hsi
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F huang-etal-2017-ntu
%X 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.
%R 10.18653/v1/S17-2177
%U https://aclanthology.org/S17-2177
%U https://doi.org/10.18653/v1/S17-2177
%P 1010-1013
Markdown (Informal)
[NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation](https://aclanthology.org/S17-2177) (Huang et al., SemEval 2017)
ACL