A General-Purpose Tagger with Convolutional Neural Networks

Xiang Yu, Agnieszka Falenska, Ngoc Thang Vu


Abstract
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.
Anthology ID:
W17-4118
Volume:
Proceedings of the First Workshop on Subword and Character Level Models in NLP
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
SCLeM | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–129
Language:
URL:
https://aclanthology.org/W17-4118
DOI:
10.18653/v1/W17-4118
Bibkey:
Cite (ACL):
Xiang Yu, Agnieszka Falenska, and Ngoc Thang Vu. 2017. A General-Purpose Tagger with Convolutional Neural Networks. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 124–129, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A General-Purpose Tagger with Convolutional Neural Networks (Yu et al., 2017)
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PDF:
https://aclanthology.org/W17-4118.pdf