TY - GEN
T1 - Identification, characterization, and grounding of gradable terms in clinical text
AU - Shivade, Chaitanya
AU - De Marneffe, Marie Catherine
AU - Fosler-Lussier, Eric
AU - Lai, Albert M.
N1 - Funding Information:
We would like to thank Courtney Hebert and Kelly Regan for their help in this work. Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under award number R01LM011116. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© BioNLP 2016. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Gradable adjectives are inherently vague and are used by clinicians to document medical interpretations (e.g., severe reaction, mild symptoms). We present a comprehensive study of gradable adjectives used in the clinical domain. We automatically identify gradable adjectives and demonstrate that they have a substantial presence in clinical text. Further, we show that there is a specific pattern associated with their usage, where certain medical concepts are more likely to be described using these adjectives than others. Interpretation of statements using such adjectives is a barrier in medical decision making. Therefore, we use a simple probabilistic model to ground their meaning based on their usage in context.
AB - Gradable adjectives are inherently vague and are used by clinicians to document medical interpretations (e.g., severe reaction, mild symptoms). We present a comprehensive study of gradable adjectives used in the clinical domain. We automatically identify gradable adjectives and demonstrate that they have a substantial presence in clinical text. Further, we show that there is a specific pattern associated with their usage, where certain medical concepts are more likely to be described using these adjectives than others. Interpretation of statements using such adjectives is a barrier in medical decision making. Therefore, we use a simple probabilistic model to ground their meaning based on their usage in context.
UR - http://www.scopus.com/inward/record.url?scp=85059894711&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059894711
T3 - BioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing
SP - 17
EP - 26
BT - BioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing
A2 - Cohen, Kevin Bretonnel
A2 - Demner-Fushman, Dina
A2 - Ananiadou, Sophia
A2 - Tsujii, Jun-ichi
PB - Association for Computational Linguistics (ACL)
T2 - 15th Workshop on Biomedical Natural Language Processing, BioNLP 2016
Y2 - 12 August 2016
ER -