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LREC
2008

System Evaluation on a Named Entity Corpus from Clinical Notes

8 years 11 months ago
System Evaluation on a Named Entity Corpus from Clinical Notes
This paper presents the evaluation of the dictionary look-up component of Mayo Clinic's Information Extraction system. The component was tested on a corpus of 160 free-text clinical notes which were manually annotated with the named entity disease. This kind of clinical text presents many language challenges such as fragmented sentences and heavy use of abbreviations and acronyms. The dictionary used for this evaluation was a subset of SNOMED-CT with semantic types corresponding to diseases/disorders without any augmentation. The algorithm achieves an F-score of 0.56 for exact matches and F-scores of 0.76 and 0.62 for right and left-partial matches respectively. Machine learning techniques are currently under investigation to improve this task.
Karin Schuler, Vinod Kaggal, James J. Masanz, Phil
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where LREC
Authors Karin Schuler, Vinod Kaggal, James J. Masanz, Philip V. Ogren, Guergana K. Savova
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