@inproceedings{long-etal-2017-xjnlp,
title = "{XJNLP} at {S}em{E}val-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model",
author = "Long, Yu and
Li, Zhijing and
Wang, Xuan and
Li, Chen",
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/S17-2178",
doi = "10.18653/v1/S17-2178",
pages = "1014--1018",
abstract = "Temporality is crucial in understanding the course of clinical events from a patient{'}s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.",
}
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<abstract>Temporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.</abstract>
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%0 Conference Proceedings
%T XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model
%A Long, Yu
%A Li, Zhijing
%A Wang, Xuan
%A Li, Chen
%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 long-etal-2017-xjnlp
%X Temporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.
%R 10.18653/v1/S17-2178
%U https://aclanthology.org/S17-2178
%U https://doi.org/10.18653/v1/S17-2178
%P 1014-1018
Markdown (Informal)
[XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model](https://aclanthology.org/S17-2178) (Long et al., SemEval 2017)
ACL