Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Jul 20:2016:88-97.
eCollection 2016.

A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain

Denis Griffis  1 , Chaitanya Shivade  2 , Eric Fosler-Lussier  2 , Albert M Lai  3
Affiliations

A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain

Denis Griffis et al. AMIA Jt Summits Transl Sci Proc. .

Abstract

Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard). We find that, with the exception of the cTAKES system, the toolkits we evaluate perform noticeably worse on clinical text than on general-domain text. We identify and discuss major classes of errors, and suggest directions for future work to improve SBD methods in the clinical domain. We also make the code used for SBD evaluation in this paper available for download at http://github.com/drgriffis/SBD-Evaluation.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Average errors per 1000 sentences, by the type of terminal character in the sentence. A and B show errors created by each toolkit, calculated as a sum of the errors on each corpus weighted by the number of sentences in that corpus. C and D show errors that occurred in each corpus, averaged across the toolkits used.
Figure 2.
Figure 2.
Runtime to process clinical notes corpus.
See this image and copyright information in PMC

References

    1. Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB. Data from clinical notes: a perspective on the tension between structure and flexible documentation. J Am Med Inform Assoc. 2011;18(2):181–6. - PMC - PubMed
    1. Gillick D. Sentence boundary detection and the problem with the U.S. Proc Hum Lang Technol 2009 Annu Conf North Am Chapter Assoc Comput Linguist Companion Vol Short Pap - NAACL ‘09; 2009. p. 241.
    1. Read J, Dridan R, Oepen S, Solberg J. Sentence Boundary Detection: A Long Solved Problem?; In Proceedings of COLING; 2012.
    1. Marcus MP, Santorini B, Marcinkiewicz MA. Building a Large Annotated Corpus of English: The Penn Treebank. Comput Linguist. 1993;19(2):313–30.
    1. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008:128–44. - PubMed

LinkOut - more resources