Paper: Shallow Information Extraction from Medical Forum Data

ACL ID C10-2133
Title Shallow Information Extraction from Medical Forum Data
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010

We study a novel shallow information ex- traction problem that involves extracting sentences of a given set of topic cate- gories from medical forum data. Given a corpus of medical forum documents, our goal is to extract two related types of sentences that describe a biomedical case (i.e., medical problem descriptions and medical treatment descriptions). Such an extraction task directly generates med- ical case descriptions that can be useful in many applications. We solve the prob- lem using two popular machine learning methods Support Vector Machines (SVM) and Conditional Random Fields (CRF). We propose novel features to improve the accuracy of extraction. Experiment results show that we can obtain an accuracy of up to 75%.