NDLI: Generating links to background knowledge: a case study using narrative radiology reports

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Generating links to background knowledge: a case study using narrative radiology reports

Content Provider	ACM Digital Library
Author	van Ommering, Rob He, Jiyin Qian, Yuechen de Rijke, Maarten Sevenster, Merlijn
Abstract	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.
Starting Page	1867
Ending Page	1876
Page Count	10
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063845
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Radiology reports Automatic link generation Wikipedia
Content Type	Text
Resource Type	Article

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