title: Annotating patient clinical records with syntactic chunks and named entities: the Harvey Corpus.
abstract: The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning.
authors: Department of Informatics, University of Sussex, Brighton, BN1 9QJ UK.
authors: Aleksandar
authors: A
authors: Savkov
authors: Department of Informatics, University of Sussex, Brighton, BN1 9QJ UK.
authors: John
authors: J
authors: Carroll
authors: Department of Informatics, University of Sussex, Brighton, BN1 9QJ UK.
authors: Rob
authors: R
authors: Koeling
authors: Division of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, BN1 9PH UK.
authors: Jackie
authors: J
authors: Cassell
keywords: Annotation guidelines; Chunking; Clinical text; Corpus annotation; Named entities