Natural Language Processing Advancements By Deep Learning: A Survey
TABLE II: POS tagging state-of-the-art models evaluated on the WSJ-PTB dataset.
['Model', 'Accuracy']
For example, the deep neural network architecture named CharWNN has been developed to join word-level and character-level representations using convolutional neural networks for POS tagging [santos2014learning]. The emphasis in [santos2014learning] is the importance of character-level feature extraction as their experimental results show the necessity of employing hand-crafted features in the absence of character-level features for achieving the state-of-the-art. Sequence tagging itself includes part of speech tagging, chunking, and named entity recognition. Likewise, a globally normalized transition-based neural network architecture has been proposed for POS-tagging [andor2016globally].