A General-Purpose Tagger with Convolutional Neural Networks
Xiang Yu, Agnieszka Faleńska, Ngoc Thang Vu · 2017
We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information.The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-theart results in part-of-speech tagging, morphological tagging and supertagging.The CNN tagger is also robust against the outof-vocabulary problem; it performs well on artificially unnormalized texts.