Year: 2006
Type: conference-paper
Abstract: 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.
Authors: David McClosky, Eugene Charniak, Mark Johnson
Institution: Brown University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2163568299