Paper: Building Systematic Reviews Using Automatic Text Classification Techniques

ACL ID C10-2035
Title Building Systematic Reviews Using Automatic Text Classification Techniques
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of informa- tion. They are fundamental tools for evi- dence-based medicine. In this paper, we show that automatic text classification can be useful in building systematic reviews for medical topics to speed up the review- ing process. We propose a per-question classification method that uses an ensem- ble of classifiers that exploit the particular protocol of a systematic review. We also show that when integrating the classifier in the human workflow of building a re- view the per-question method is superior to the global method. We test several evaluation measures on a real dataset.