Hybrid text segmentation for Hungarian clinical records

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. © Springer-Verlag 2013.