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Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction
2019
Proceedings of the 2019 Conference of the North
We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the
doi:10.18653/v1/n19-1113
dblp:conf/naacl/Stratos19
fatcat:2zxlqothzffi3mf4drq6zvrvze