@inproceedings{879b7a7e48ab4595992870e884759f17,
title = "Unsupervised Domain Adaptation for Clinical Negation Detection",
abstract = "Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.",
author = "Miller, \{Timothy A.\} and Steven Bethard and Hadi Amiri and Guergana Savova",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics; 16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017 ; Conference date: 04-08-2017",
year = "2017",
language = "English (US)",
series = "BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "165--170",
booktitle = "BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop",
}