Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 61, Issue 3, Pages 131-136Publisher
ELSEVIER
DOI: 10.1016/j.artmed.2014.02.002
Keywords
Natural language processing; Automatic syntactic analysis; Dependency parsing; Information extraction; Domain-adaptation; Clinical language variants; Finnish language
Funding
- Academy of Finland
- Emil Aaltonen foundation
Ask authors/readers for more resources
Objectives: In this paper, we study the development and domain-adaptation of statistical syntactic parsers for three different clinical domains in Finnish. Methods and materials: The materials include text from daily nursing notes written by nurses in an intensive care unit, physicians' notes from cardiology patients' health records, and daily nursing notes from cardiology patients' health records. The parsing is performed with the statistical parser of Bohnet (http://code.google.com/p/mate-tools/, accessed: 22 November 2013). Results:A parser trained only on general language performs poorly in all clinical subdomains, the labelled attachment score (LAS) ranging from 59.4% to 71.4%, whereas domain data combined with general language gives better results, the LAS varying between 67.2% and 81.7%. However, even a small amount of clinical domain data quickly outperforms this and also clinical data from other domains is more beneficial (LAS 71.3-80.0%) than general language only. The best results (LAS 77.4-84.6%) are achieved by using as training data the combination of all the clinical treebanks. Conclusions: In order to develop a good syntactic parser for clinical language variants, a general language resource is not mandatory, while data from clinical fields is. However, in addition to the exact same clinical domain, also data from other clinical domains is useful. (C) 2014 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available