{"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/toward-a-standardized-and-more-accurate","title":"Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging","arxiv_id":"1809.03391","date":"2018-09-10","proceeding":null,"authors":["Kemal Kurniawan","Alham Fikri Aji"],"abstract":"Previous work in Indonesian part-of-speech (POS) tagging are hard to compare\nas they are not evaluated on a common dataset. Furthermore, in spite of the\nsuccess of neural network models for English POS tagging, they are rarely\nexplored for Indonesian. In this paper, we explored various techniques for\nIndonesian POS tagging, including rule-based, CRF, and neural network-based\nmodels. We evaluated our models on the IDN Tagged Corpus. A new\nstate-of-the-art of 97.47 F1 score is achieved with a recurrent neural network.\nTo provide a standard for future work, we release the dataset split that we\nused publicly.","url_abs":"http://arxiv.org/abs/1809.03391v3","url_pdf":"http://arxiv.org/pdf/1809.03391v3.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":[{"paper_slug":"toward-a-standardized-and-more-accurate","repo_url":"https://github.com/kmkurn/id-pos-tagging","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.03391","atlas_url":"https://app.syntology.ai/?focus=1809.03391","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}