{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/part-of-speech-tagging-for-twitter-with","title":"Part-of-Speech Tagging for Twitter with Adversarial Neural Networks","arxiv_id":null,"date":"2017-09-01","proceeding":"EMNLP 2017 9","authors":["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.","url_abs":"https://aclanthology.org/D17-1256","url_pdf":"https://aclanthology.org/D17-1256.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"stock-prediction","task_name":"Stock Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/part-of-speech-tagging-on-ritter","task":"Part-Of-Speech Tagging","dataset":"Ritter","model":"Gui et al., 2017","rank_in_archive_order":3,"of":4,"metrics":{"Acc":"90.9"},"uses_additional_data":false},{"leaderboard":"/sota/part-of-speech-tagging-on-tweebank","task":"Part-Of-Speech Tagging","dataset":"Tweebank","model":"Gui et al., 2017","rank_in_archive_order":3,"of":3,"metrics":{"Acc":"92.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}