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
- Editors:
- Manaal Faruqui, Hinrich Schuetze, Isabel Trancoso, Yadollah Yaghoobzadeh
- Venue:
- SCLeM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 124–129
- Language:
- URL:
- https://aclanthology.org/W17-4118
- DOI:
- 10.18653/v1/W17-4118
- 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., SCLeM 2017)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W17-4118.pdf