Paper: Medical Relation Extraction with Manifold Models

ACL ID P14-1078
Title Medical Relation Extraction with Manifold Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

In this paper, we present a manifold model for medical relation extraction. Our model is built upon a medical corpus containing 80M sentences (11 gigabyte text) and de- signed to accurately and efficiently detect the key medical relations that can facilitate clinical decision making. Our approach integrates domain specific parsing and typ- ing systems, and can utilize labeled as well as unlabeled examples. To provide users with more flexibility, we also take label weight into consideration. Effectiveness of our model is demonstrated both theo- retically with a proof to show that the so- lution is a closed-form solution and exper- imentally with positive results in experi- ments.