Adapting Self-Training for Semantic Role Labeling

Supervised semantic role labeling (SRL) systems trained on hand-crafted annotated corpora have recently achieved state-of-the-art performance. However, creating such corpora is tedious and costly, with the resulting corpora not sufficiently representative of the language. This paper describes a part of an ongoing work on applying bootstrapping methods to SRL to deal with this problem. Previous work shows that, due to the complexity of SRL, this task is not straight forward. One major difficulty is the propagation of classification noise into the successive iterations. We address this problem by employing balancing and preselection methods for self-training, as a bootstrapping algorithm. The proposed methods could achieve improvement over the base line, which do not use these methods.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Rasoul Samad Zadeh Kaljahi
• Keywords: ACL 2010, Bootstrapping Algorithm, Computational Linguistics, Semantic Role, Successive Iterations