Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, Xuanjing Huang
Abstract
In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.- Anthology ID:
- D17-1256
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2411–2420
- Language:
- URL:
- https://aclanthology.org/D17-1256
- DOI:
- 10.18653/v1/D17-1256
- Cite (ACL):
- Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, and Xuanjing Huang. 2017. Part-of-Speech Tagging for Twitter with Adversarial Neural Networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2411–2420, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Part-of-Speech Tagging for Twitter with Adversarial Neural Networks (Gui et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D17-1256.pdf
- Data
- Tweebank