Abstract
We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1%, an absolute 1.1% improvement (12% error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon. © 2006 Association for Computational Linguistics.
Cite
CITATION STYLE
McClosky, D., Charniak, E., & Johnson, M. (2006). Effective self-training for parsing. In HLT-NAACL 2006 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings of the Main Conference (pp. 152–159). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220835.1220855
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
