@inproceedings{f7e2309e6c6a4b7588ae159be36163cc,
title = "CUAB: Supervised Learning of Disorders and their Attributes Using Relations",
abstract = "We implemented an end-to-end system for disorder identification and slot filling. For identifying spans for both disorders and their attributes, we used a linear chain conditional random field (CRF) approach coupled with cTAKES for pre-processing. For combining disjoint disorder spans, finding relations between attributes and disorders, and attribute normalization, we used l2-regularized l2-loss linear support vector machine (SVM) classification. Disorder CUIs were identified using a back-off approach to YTEX lookup (CUAB1) or NLM UTS API (CUAB2) if the target text was not found in the training data. Our best system utilized UMLS semantic type features for disorder/attribute span identification and the NLM UTS API for normalization. It was ranked 12th in Task 1 (disorder identification) and 6th in Task 2b (disorder identification and slot filling) with a weighted F Measure of 0.711.",
author = "James Gung and Osborne, {John David} and Steven Bethard",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics; 9th International Workshop on Semantic Evaluation, SemEval 2015 ; Conference date: 04-06-2015 Through 05-06-2015",
year = "2015",
language = "English (US)",
series = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "417--421",
editor = "Preslav Nakov and Torsten Zesch and Daniel Cer and David Jurgens",
booktitle = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics",
}