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Author Huang, Ruihong ♦ Riloff, Ellen
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Message Board ♦ Extracted Lexicon ♦ Patient Attribute ♦ Pa-tient Information ♦ Veterinary Patient Attribute ♦ Large Collection ♦ Information Extrac-tion ♦ Ie System ♦ Text Classifier Yield ♦ Specific Patient ♦ Veterinary Patient ♦ Small Amount ♦ Text Classifier ♦ Veterinary Patient Attribute Term ♦ Unannotated Text ♦ Vet-erinary Message Board ♦ Experimental Result ♦ Gen-erated Attribute List ♦ General Question ♦ Learned Attribute List
Abstract The goal of our research is to distinguish vet-erinary message board posts that describe a case involving a specific patient from posts that ask a general question. We create a text classifier that incorporates automatically gen-erated attribute lists for veterinary patients to tackle this problem. Using a small amount of annotated data, we train an information extrac-tion (IE) system to identify veterinary patient attributes. We then apply the IE system to a large collection of unannotated texts to pro-duce a lexicon of veterinary patient attribute terms. Our experimental results show that us-ing the learned attribute lists to encode pa-tient information in the text classifier yields improved performance on this task. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study