Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, Xuanjing Huang · 2017
In this work, we study the problem of partof-speech tagging for Tweets.In contrast to newswire articles, Tweets are usually informal and contain numerous out-ofvocabulary 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 indomain 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.