Paper: Classifying Message Board Posts with an Extracted Lexicon of Patient Attributes

ACL ID D13-1162
Title Classifying Message Board Posts with an Extracted Lexicon of Patient Attributes
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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.