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A high-performance semi-supervised learning method for text chunking
2005
Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. The idea is to find "what good classifiers are like" by learning from thousands of automatically generated auxiliary
doi:10.3115/1219840.1219841
dblp:conf/acl/AndoZ05
fatcat:b222o7nponbn3dqifbdci5ng2i