A General-Purpose Tagger with Convolutional Neural Networks
This provides a robust, general-purpose solution for natural language processing tasks, benefiting researchers and practitioners in computational linguistics, though it is incremental as it builds on existing CNN methods.
The authors tackled the problem of general-purpose tagging across multiple tasks using a convolutional neural network (CNN) approach, achieving state-of-the-art results in part-of-speech tagging, morphological tagging, and supertagging without task-specific hyper-parameter tuning, and it performed well on out-of-vocabulary and unnormalized texts.
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-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.