Chinese Chunking with Tri-Training Learning
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BibTeX
@MISC{Chen_chinesechunking,
author = {Wenliang Chen and Yujie Zhang and Hitoshi Isahara},
title = {Chinese Chunking with Tri-Training Learning},
year = {}
}
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Abstract
Abstract. This paper presents a practical tri-training method for Chinese chunking using a small amount of labeled training data and a much larger pool of unlabeled data. We propose a novel selection method for tri-training learning in which newly labeled sentences are selected by comparing the agreements of three classifiers. In detail, in each iteration, a new sample is selected for a classifier if the other two classifiers agree on the labels while itself disagrees. We compare the proposed tri-training learning approach with co-training learning approach on Upenn Chinese Treebank V4.0(CTB4). The experimental results show that the proposed approach can improve the performance significantly. 1
