Abstract
Abbreviations pose a challenge for information extraction systems. In clinical text, abbreviations are abundant, as this type of documentation is written under time-pressure. We report work on characterizing abbreviations in Swedish clinical text and the development of SCAN: a Swedish Clinical Abbreviation Normalizer, which is built for the purpose of improving information access systems in the clinical domain. The clinical domain includes several subdomains with differing vocabularies depending on the nature of the specialist work, and adaption of NLP-tools may consequently be necessary. We extend and adapt SCAN, and evaluate on two different clinical subdomains: emergency department (ED) and radiology (X-ray). Overall final results are 85% (ED) and 83% (X-ray) F1-measure on the task of abbreviation identification. We also evaluate coverage of abbreviation expansion candidates in existing lexical resources, and create two new, freely available, lexicons with abbreviations and their possible expansions for the two clinical subdomains.
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Kvist, M., Velupillai, S. (2014). SCAN: A Swedish Clinical Abbreviation Normalizer. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_7
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DOI: https://doi.org/10.1007/978-3-319-11382-1_7
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