Papers › Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, Xuanjing Huang
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Part-Of-Speech Tagging | Ritter | Gui et al., 2017 | Acc | 90.9 | #3 of 4 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Tweebank | Gui et al., 2017 | Acc | 92.8 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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