{"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/fast-and-accurate-neural-word-segmentation","title":"Fast and Accurate Neural Word Segmentation for Chinese","arxiv_id":"1704.07047","date":"2017-04-24","proceeding":"ACL 2017 7","authors":["Deng Cai","Hai Zhao","Zhisong Zhang","Yuan Xin","Yongjian Wu","Feiyue Huang"],"abstract":"Neural models with minimal feature engineering have achieved competitive\nperformance against traditional methods for the task of Chinese word\nsegmentation. However, both training and working procedures of the current\nneural models are computationally inefficient. This paper presents a greedy\nneural word segmenter with balanced word and character embedding inputs to\nalleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of\nperforming segmentation much faster and even more accurate than\nstate-of-the-art neural models on Chinese benchmark datasets.","url_abs":"http://arxiv.org/abs/1704.07047v1","url_pdf":"http://arxiv.org/pdf/1704.07047v1.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":"fast-and-accurate-neural-word-segmentation","repo_url":"https://github.com/jcyk/greedyCWS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"chinese-word-segmentation","task_name":"Chinese Word Segmentation"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.07047","atlas_url":"https://app.syntology.ai/?focus=1704.07047","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}