Paper: Tagging The Web: Building A Robust Web Tagger with Neural Network

ACL ID P14-1014
Title Tagging The Web: Building A Robust Web Tagger with Neural Network
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we address the problem of web-domain POS tagging using a two- phase approach. The first phase learns rep- resentations that capture regularities un- derlying 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 accu- racy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.