COVID-19 Named Entity Recognition for Vietnamese

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2146–2153 June 6–11, 2021. ©2021 Association for Computational Linguistics 2146 COVID-19 Named Entity Recognition for Vietnamese Thinh Hung Truong, Mai Hoang Dao and Dat Quoc Nguyen VinAI Research, Hanoi, Vietnam {v.thinhth88, v.maidh3, v.datnq9}@vinai.io Abstract The current COVID-19 pandemic has lead to the creation of many corpora that facil- itate NLP research and downstream appli- cations to help fight the pandemic. How- ever, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 re- lated 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 exper- iments using strong baselines on our dataset, and find that: automatic Vietnamese word seg- mentation 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. 1 Introduction As of early November 2020, the total number of COVID-19 cases worldwide has surpassed 50M.1 The world is once again hit by a new wave of COVID-19 infection with record-breaking num- bers of new cases reported everyday. Along with the outbreak of the pandemic, information about the COVID-19 is aggregated rapidly through dif- ferent types of texts in different languages (Aizawa et al., 2020). Particularly, in Vietnam, text reports containing official information from the govern- ment about COVID-19 cases are presented in great 1https://www.worldometers.info/coronavirus/worldwide- graphs/#total-cases detail, including de-identified personal informa- tion, travel history, as well as information of people who come into contact with the cases. The reports are frequently kept up to date at reputable online news sources, playing a significant role to help the country combat the pandemic. It is thus essential to build systems to retrieve and condense infor- mation from those official sources so that related people and organizations can promptly grasp the key information for epidemic prevention tasks, and the systems should also be able to adapt and sync quickly with epidemics that take place in the future. One of the first steps to develop such systems is to recognize relevant named entities mentioned in the texts, which is also known as the NER task. Compared to other languages, data resources for the Vietnamese NER task are limited, includ- ing only two public datasets from the VLSP 2016 and 2018 NER shared tasks (Huyen and Luong, 2016; Nguyen et al., 2018b). Here, the VLSP-2018 NER dataset is an extension of the VLSP-2016 NER dataset with more data. These two datasets only focus on …

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