Parsing clinical text: how good are the state-of-the-art parsers?

BMC Medical Informatics and Decision Making

Research Article

Josh Denny¹  Yang Huang²  Jung-wei Fan²  Buzhou Tang³  Hua Xu⁴  Min Jiang⁴ 

[1] Department of Medicine, Vanderbilt University, School of Medicine Nashville, TN, USA;Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, TN, USA;Kaiser Permanente, San Diego, CA, USA;Shenzhen Graduate School of Harbin institute of Technology, Shenzhen, China;The University of Texas School of Biomedical Informatics at Houston, Houston, TX, USA;

关键词: Medical language processing;    natural language processing;    parsing;    clinical text;    NLP;   

DOI  :  10.1186/1472-6947-15-S1-S2

来源: Springer

【 摘 要 】

BackgroundParsing, which generates a syntactic structure of a sentence (a parse tree), is a critical component of natural language processing (NLP) research in any domain including medicine. Although parsers developed in the general English domain, such as the Stanford parser, have been applied to clinical text, there are no formal evaluations and comparisons of their performance in the medical domain.MethodsIn this study, we investigated the performance of three state-of-the-art parsers: the Stanford parser, the Bikel parser, and the Charniak parser, using following two datasets: (1) A Treebank containing 1,100 sentences that were randomly selected from progress notes used in the 2010 i2b2 NLP challenge and manually annotated according to a Penn Treebank based guideline; and (2) the MiPACQ Treebank, which is developed based on pathology notes and clinical notes, containing 13,091 sentences. We conducted three experiments on both datasets. First, we measured the performance of the three state-of-the-art parsers on the clinical Treebanks with their default settings. Then we re-trained the parsers using the clinical Treebanks and evaluated their performance using the 10-fold cross validation method. Finally we re-trained the parsers by combining the clinical Treebanks with the Penn Treebank.ResultsOur results showed that the original parsers achieved lower performance in clinical text (Bracketing F-measure in the range of 66.6%-70.3%) compared to general English text. After retraining on the clinical Treebank, all parsers achieved better performance, with the best performance from the Stanford parser that reached the highest Bracketing F-measure of 73.68% on progress notes and 83.72% on the MiPACQ corpus using 10-fold cross validation. When the combined clinical Treebanks and Penn Treebank was used, of the three parsers, the Charniak parser achieved the highest Bracketing F-measure of 73.53% on progress notes and the Stanford parser reached the highest F-measure of 84.15% on the MiPACQ corpus.ConclusionsOur study demonstrates that re-training using clinical Treebanks is critical for improving general English parsers' performance on clinical text, and combining clinical and open domain corpora might achieve optimal performance for parsing clinical text.

【 授权许可】

Unknown   
© Jiang et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.