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Hybrid Text Segmentation for Hungarian Clinical Records

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Advances in Artificial Intelligence and Its Applications (MICAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8265))

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Abstract

Nowadays clinical documents are getting widely available to researchers who are aiming to develop resources and tools that may help clinicians in their work. While several attempts exist for English medical text processing, there are only few for other languages. Moreover, word and sentence segmentation tasks are commonly treated as simple engineering issues. In this study, we introduce the difficulties that arise during the segmentation of Hungarian clinical records, and describe a complex method that results in a normalized and segmented text. Our approach is a hybrid combination of a rule-based and an unsupervised statistical solution. The presented system is compared with other algorithms that are available and commonly used. These fail to segment clinical text (all of them reach F-scores below 75%), while our method scores above 90%. This means that only the hybrid tool described in this study can be used for the segmentation of Hungarian clinical texts in practical applications.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 50/a Práter street, 1083, Budapest, Hungary

    György Orosz, Attila Novák & Gábor Prószéky

  2. MTA-PPKE Hungarian Language Technology Research Group, Pázmány Péter Catholic University, 50/a Práter street, 1083, Budapest, Hungary

    György Orosz, Attila Novák & Gábor Prószéky

Authors
  1. György Orosz
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  2. Attila Novák
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  3. Gábor Prószéky
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Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Ciudad Universitaria,, Carretera Pachuca–Tulancingo km 4.5, Hidalgo, Mexico

    Félix Castro

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  3. Tecnológico de Monterrey, Campus Estado de México,, Carretera Lago de Guadalupe Km 3.5, Atizapán de Zaragoza,, CP 52926, Estado de México, Mexico

    Miguel González

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Orosz, G., Novák, A., Prószéky, G. (2013). Hybrid Text Segmentation for Hungarian Clinical Records. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45114-0_25

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  • DOI: https://doi.org/10.1007/978-3-642-45114-0_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45113-3

  • Online ISBN: 978-3-642-45114-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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