Self-training without reranking for parser domain adaptation and its impact on semantic role labeling

Authors:
Kenji Sagae
Affiliations:
University of Southern California, Marina del Rey, CA
Venue:
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Year:
2010

Citing 12
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Abstract

We compare self-training with and without reranking for parser domain adaptation, and examine the impact of syntactic parser adaptation on a semantic role labeling system. Although self-training without reranking has been found not to improve in-domain accuracy for parsers trained on the WSJ Penn Treebank, we show that it is surprisingly effective for parser domain adaptation. We also show that simple self-training of a syntactic parser improves out-of-domain accuracy of a semantic role labeler.