Abstract
Compounding is widespread in highly inflectional languages with a quarter of all nouns created by composition. In our field of study, the German medical language, the amount of compounds significantly outnumbers this figure with 64 %. Thus, their correct splitting is a high-impact preprocessing step for any NLP-based application. In this work we address two challenges of medical decomposition: First, we introduce the consideration of unknown constituents in order to split compounds that were not recognized as such so far. Second, our approach builds on the corpus-based approach of Koehn and Knight and adds semantic knowledge from domain ontologies to increase the accuracy during disambiguation of the various split options. Using this first-of-a-kind semantic approach in a study on decomposition of German medical compounds, we outperform the existing approaches by far.
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Bretschneider, C., Zillner, S. (2015). Semantic Splitting of German Medical Compounds. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_24
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DOI: https://doi.org/10.1007/978-3-319-24033-6_24
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