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        <title>Part-of-Speech Tagging for Twitter with Adversarial Neural Networks</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
%A Gui, Tao
%A Zhang, Qi
%A Huang, Haoran
%A Peng, Minlong
%A Huang, Xuanjing
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F gui-etal-2017-part
%X 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.
%R 10.18653/v1/D17-1256
%U https://aclanthology.org/D17-1256
%U https://doi.org/10.18653/v1/D17-1256
%P 2411-2420

Markdown (Informal)

[Part-of-Speech Tagging for Twitter with Adversarial Neural Networks](https://aclanthology.org/D17-1256) (Gui et al., EMNLP 2017)

• Part-of-Speech Tagging for Twitter with Adversarial Neural Networks (Gui et al., EMNLP 2017)

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.