- Generating links to background knowledge: a case study using narrative radiology reports
- 2011 ACM International Conference on Information and Knowledge Management
- Book/source title
- CIKM'11: proceedings of the 2011 ACM International Conference on Information and Knowledge Management: October 24-28, 2011, Glasgow, Scotland, UK
- Pages (from-to)
- New York, NY: Association for Computing Machinery
- Document type
- Conference contribution
- Faculty of Science (FNWI)
Interfacultary Research Institutes
- Informatics Institute (IVI)
Institute for Logic, Language and Computation (ILLC)
Automatically annotating texts with background information has recently received much attention. We conduct a case study in automatically generating links from narrative radiology reports to Wikipedia. Such links help users understand the medical terminology and thereby increase the value of the reports. Direct applications of existing automatic link generation systems trained on Wikipedia to our radiology data do not yield satisfactory results. Our analysis reveals that medical phrases are often syntactically regular but semantically complicated, e.g., containing multiple concepts or concepts with multiple modifiers. The latter property is the main reason for the failure of existing systems. Based on this observation, we propose an automatic link generation approach that takes into account these properties. We use a sequential labeling approach with syntactic features for anchor text identification in order to exploit syntactic regularities in medical terminology. We combine this with a sub-anchor based approach to target finding, which is aimed at coping with the complex semantic structure of medical phrases. Empirical results show that the proposed system effectively improves the performance over existing systems.
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