Classifying message board posts with an extracted lexicon of patient attributes
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2013 Association for Computational Linguistics. The goal of our research is to distinguish veterinary 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 generated attribute lists for veterinary patients to tackle this problem. Using a small amount of annotated data, we train an information extraction (IE) system to identify veterinary patient attributes. We then apply the IE system to a large collection of unannotated texts to produce a lexicon of veterinary patient attribute terms. Our experimental results show that using the learned attribute lists to encode patient information in the text classifier yields improved performance on this task.
EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
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