Generating links to background knowledge: A case study using narrative radiology reports
|Generating links to background knowledge: A case study using narrative radiology reports|
|Author(s)||He J., De Rijke M., Sevenster M., Van Ommering R., Qian Y.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||automatic link generation, radiology reports, wikipedia (Extra: Automatic link generation, Background information, Background knowledge, Complex semantic structures, Empirical results, Existing systems, Medical terminologies, radiology reports, Syntactic features, Target finding, Text identification, Wikipedia, Knowledge management, Radiology, Semantics, Syntactics, Terminology, Radiation)|
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Generating links to background knowledge: A case study using narrative radiology reports is a 2011 conference paper written in English by He J., De Rijke M., Sevenster M., Van Ommering R., Qian Y. and published in International Conference on Information and Knowledge Management, Proceedings.
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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