Negation's Not Solved: Reconsidering Negation Annotation and Evaluation

s (1,273 abstracts also in the GENIA corpus). Here, the scope of negation is specified as the maximum span within which the negation cue word could be applicable, and the scope cannot be disjoint from the cue word. This is in contrast to the negation annotations we explore; we do not explore scope annotations for two reasons: First, the lack of gold standard named entity mentions is an additional source of error that no other corpus would have, making the comparison unfair. Second, while such scope annotations overcome some recall issues for

[1]  Ilya M. Goldin,et al.  Learning to Detect Negation with ‘Not’ in Medical Texts , 2003 .

[2]  Rodney D. Nielsen,et al.  The MiPACQ clinical question answering system. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[3]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[4]  Sunghwan Sohn,et al.  Dependency Parser-based Negation Detection in Clinical Narratives , 2012, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[5]  Peter L. Elkin,et al.  A controlled trial of automated classification of negation from clinical notes , 2005, BMC Medical Informatics Decis. Mak..

[6]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[7]  Yang Huang,et al.  A novel hybrid approach to automated negation detection in clinical radiology reports. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[8]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[9]  Olivier Bodenreider,et al.  Exploring semantic groups through visual approaches , 2003, J. Biomed. Informatics.

[10]  Matthew Scotch,et al.  The Yale cTAKES extensions for document classification: architecture and application , 2011, J. Am. Medical Informatics Assoc..

[11]  David Tresner-Kirsch,et al.  MITRE system for clinical assertion status classification , 2011, J. Am. Medical Informatics Assoc..

[12]  János Csirik,et al.  The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.

[13]  Wendy W. Chapman,et al.  ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports , 2009, J. Biomed. Informatics.

[14]  Shuying Shen,et al.  2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text , 2011, J. Am. Medical Informatics Assoc..

[15]  Rodney D. Nielsen,et al.  Towards comprehensive syntactic and semantic annotations of the clinical narrative , 2013, J. Am. Medical Informatics Assoc..