{"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/covid-19-named-entity-recognition-for","title":"COVID-19 Named Entity Recognition for Vietnamese","arxiv_id":"2104.03879","date":"2021-04-08","proceeding":"NAACL 2021 4","authors":["Thinh Hung Truong","Mai Hoang Dao","Dat Quoc Nguyen"],"abstract":"The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other than English. In this paper, we present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is annotated for the named entity recognition (NER) task with newly-defined entity types that can be used in other future epidemics. Our dataset also contains the largest number of entities compared to existing Vietnamese NER datasets. We empirically conduct experiments using strong baselines on our dataset, and find that: automatic Vietnamese word segmentation helps improve the NER results and the highest performances are obtained by fine-tuning pre-trained language models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) produces higher results than the multilingual model XLM-R (Conneau et al., 2020). We publicly release our dataset at: https://github.com/VinAIResearch/PhoNER_COVID19","url_abs":"https://arxiv.org/abs/2104.03879v1","url_pdf":"https://arxiv.org/pdf/2104.03879v1.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":"covid-19-named-entity-recognition-for","repo_url":"https://github.com/VinAIResearch/PhoNER_COVID19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"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":"named-entity-recognition-in-vietnamese","task_name":"Named Entity Recognition In Vietnamese"},{"task_slug":"vietnamese-word-segmentation","task_name":"Vietnamese Word Segmentation"},{"task_slug":"xlm-r","task_name":"XLM-R"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"xlm-r","method_name":"XLM-R"}],"datasets_introduced":[{"slug":"phoner-covid19","name":"PhoNER COVID19","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-in-vietnamese-on-1","task":"Named Entity Recognition In Vietnamese","dataset":"PhoNER COVID19","model":"PhoBERT","rank_in_archive_order":2,"of":3,"metrics":{"F1 (%)":"94.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.03879","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}