@inproceedings{8590ffae26fb4e558cb9d37fbb5780f0,
title = "Domain adaptation in practice: Lessons from a real-world information extraction pipeline",
abstract = "Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs. In this work, we describe an effort to do just that – combining state-of-the-art neural methods for negation detection, document time relation extraction, and aspectual link prediction, with the eventual goal of extracting drug timelines from electronic health record text. We train on the THYME colon cancer corpus and test on both the THYME brain cancer corpus and an internal corpus, and show that performance of the combined systems is unacceptable despite good performance of individual systems. Although domain adaptation shows improvements on each individual system, the model selection problem is a barrier to improving overall pipeline performance.",
author = "Timothy Miller and Egoitz Laparra and Steven Bethard",
note = "Funding Information: Research reported in this publication was supported by the National Library Of Medicine of the National Institutes of Health under Award Numbers R01LM012918 and R01LM012973. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; 2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021 ; Conference date: 20-04-2021",
year = "2021",
language = "English (US)",
series = "Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "105--110",
editor = "Eyal Ben-David and Shay Cohen and Ryan McDonald and Barbara Plank and Roi Reichart and Guy Rotman and Yftah Ziser",
booktitle = "Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings",
}