Paper: A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents

ACL ID D11-1025
Title A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Argumentative Zoning (AZ) – analysis of the argumentative structure of a scientific paper – has proved useful for a number of informa- tion access tasks. Current approaches to AZ rely on supervised machine learning (ML). Requiring large amounts of annotated data, these approaches are expensive to develop and port to different domains and tasks. A poten- tial solution to this problem is to use weakly- supervised ML instead. We investigate the performance of four weakly-supervised clas- sifiers on scientific abstract data annotated for multiple AZ classes. Our best classifier based on the combination of active learning and self- training outperforms our best supervised clas- sifier, yielding a high accuracy of 81% when using just 10% of the labeled data. This re- sult suggests that weakl...