{"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/a-feature-rich-vietnamese-named-entity","title":"A Feature-Rich Vietnamese Named-Entity Recognition Model","arxiv_id":"1803.04375","date":"2018-03-12","proceeding":null,"authors":["Pham Quang Nhat Minh"],"abstract":"In this paper, we present a feature-based named-entity recognition (NER)\nmodel that achieves the start-of-the-art accuracy for Vietnamese language. We\ncombine word, word-shape features, PoS, chunk, Brown-cluster-based features,\nand word-embedding-based features in the Conditional Random Fields (CRF) model.\nWe also explore the effects of word segmentation, PoS tagging, and chunking\nresults of many popular Vietnamese NLP toolkits on the accuracy of the proposed\nfeature-based NER model. Up to now, our work is the first work that\nsystematically performs an extrinsic evaluation of basic Vietnamese NLP\ntoolkits on the downstream NER task. Experimental results show that while\nautomatically-generated word segmentation is useful, PoS and chunking\ninformation generated by Vietnamese NLP tools does not show their benefits for\nthe proposed feature-based NER model.","url_abs":"http://arxiv.org/abs/1803.04375v1","url_pdf":"http://arxiv.org/pdf/1803.04375v1.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":"a-feature-rich-vietnamese-named-entity","repo_url":"https://github.com/minhpqn/vietner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"chunking","task_name":"Chunking"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"model","task_name":"model"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}