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.
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Chen, W., Zhang, Y., Isahara, H. (2006). Chinese Chunking with Tri-training Learning. In: Matsumoto, Y., Sproat, R.W., Wong, KF., Zhang, M. (eds) Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead. ICCPOL 2006. Lecture Notes in Computer Science(), vol 4285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11940098_49
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DOI: https://doi.org/10.1007/11940098_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49667-0
Online ISBN: 978-3-540-49668-7
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