Paper: Guiding Semi-Supervision with Constraint-Driven Learning

ACL ID P07-1036
Title Guiding Semi-Supervision with Constraint-Driven Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007

Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algo- rithms as a way to train classi ers when the labeled data is scarce, and the study of ways to exploit knowledge and global information in structured learning tasks. In this paper, we suggest a method for incorporating do- main knowledge in semi-supervised learn- ing algorithms. Our novel framework uni es and can exploit several kinds of task speci c constraints. The experimental results pre- sented in the information extraction domain demonstrate that applying constraints helps the model to generate better feedback during learning, and hence the framework allows for high performance learning with signif- icantly less training ...