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Generating links to background knowledge: A case study using narrative radiology reports
Abstract Automatically annotating texts with backgrAutomatically 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.ves the performance over existing systems.
Abstractsub Automatically annotating texts with backgrAutomatically 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.ves the performance over existing systems.
Bibtextype inproceedings  +
Doi 10.1145/2063576.2063845  +
Has author He J. + , Maarten de Rijke + , Sevenster M. + , Van Ommering R. + , Qian Y. +
Has extra keyword 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 +
Has keyword Automatic link generation + , Radiology reports + , Wikipedia +
Isbn 9781450307178  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1867–1876  +
Published in International Conference on Information and Knowledge Management, Proceedings +
Title Generating links to background knowledge: A case study using narrative radiology reports +
Type conference paper  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:00:28  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 19:00:28  +
DateThis property is a special property in this wiki. 2011  +
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