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- 764 creator guergana-savova.
- 764 creator james-masanz.
- 764 creator karin-schuler.
- 764 creator philip-ogren.
- 764 creator vinod-kaggal.
- 764 type InProceedings.
- 764 label "System Evaluation on a Named Entity Corpus from Clinical Notes".
- 764 sameAs 764.
- 764 abstract "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.".
- 764 hasAuthorList authorList.
- 764 hasTopic Linguistics.
- 764 isPartOf proceedings.
- 764 keyword "Corpus (creation, annotation, etc.)".
- 764 keyword "Named Entity recognition".
- 764 keyword "Tools, systems, applications".
- 764 title "System Evaluation on a Named Entity Corpus from Clinical Notes".