Tagging The Web: Building A Robust Web Tagger with Neural Network
Ji Ma, Yue Zhang, Jingbo Zhu · 2014
In this paper, we address the problem of web-domain POS tagging using a twophase approach.The first phase learns representations that capture regularities underlying web text.The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger.Parameters of the neural network are trained using guided learning in the second phase.Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.