Effective self-training for parsing
摘要
We present a simple, but surprisingly effective, method of self-training a twophase 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.
引用本文（GB/T 7714）
David McClosky, Eugene Charniak, Mark Johnson. Effective self-training for parsing[J]. 未知来源, 2006.
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