2007 GuidingSemi-SuperwithConstrDrivenLea
- (Chang et al., 2007) ⇒ Ming-Wei Chang, Lev Ratinov, and Dan Roth. (2007). “Guiding Semi-Supervision with Constraint-Driven Learning.” In: Proceedings of the Annual Meeting of the ACL (ACL 2007).
Subject Headings: Constraint-Driven Learning, Semi-Supervised Learning Algorithm.
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Abstract
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 algorithms as a way to train classifiers 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 domain knowledge in semi-supervised learning algorithms. Our novel framework unifies and can exploit several kinds of task specific constraints. The experimental results presented 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 significantly less training data than was possible before on these tasks.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2007 GuidingSemi-SuperwithConstrDrivenLea | Ming-Wei Chang Lev Ratinov | Guiding Semi-Supervision with Constraint-Driven Learning | http://acl.ldc.upenn.edu/P/P07/P07-1036.pdf |