COVID -19 Named Entity Recognition for V ietnamese (2021)

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

## 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 numbers of new cases reported everyday. along with the outbreak of the pandemic, information about the covid-19 is aggregated rapidly through different types of texts in different languages @xcite . particularly, in vietnam, text reports containing official information from the government about covid-19 cases are presented in great detail, including de-identified personal information, 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 information 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, including only two public datasets from the vlsp 2016 and 2018 ner shared tasks @xcite . here, the vlsp-2018 ner dataset is an extension of the vlsp-2016 ner dataset with more data. these two datasets only focus on recognizing generic entities of person names, organizations, and locations in online news articles. thus, making them difficult to adapt to the context of extracting key entity information related to covid-19 patients. this leads to our work's main goals that are: (i) to develop a ner task in the covid-19 specified domain, that potentially impacts research and downstream applications, and (ii) to provide the research community with a new dataset for recognizing covid-19 related named entities in vietnamese. in this paper, we present a named entity annotated dataset with newly-defined entity types that can be applied to future epidemics. the dataset contains informative sentences related to covid-19, extracted from articles crawled from reputable vietnamese online news sites. here, we do not consider other types of popular social media in vietnam such as facebook as they contain much label definition patient_id unique identifier of a covid-19 patient in vietnam. an patient_id annotation over "x" refers to as the x th patient having covid-19 in vietnam. person_name name of a patient or person who comes into contact with a patient. age age of a patient or person who comes into contact with a patient. gender gender of a patient or person who comes into contact with a patient. occupation job of a patient or person who comes into contact with a patient. location locations/places that a patient was presented at. organization organizations related to a patient, e.g. company, government organization, and the like, with structures and their own functions. symptom&disease symptoms that a patient experiences, and diseases that a patient had prior to covid-19 or complications that usually appear in death reports. ## related work most covid-19 related datasets are constructed from two types of sources. the first one is scientific publications, including the datasets @xcite and litcovid @xcite , that help facilitate many types of research works, such as building search engines to retrieve relevant information from scholarly articles @xcite @xcite , question answering and summarization @xcite . recently, @xcite fine-tune a bert-based ner model on the craft corpus @xcite to recognize and then normalize biomedical ontology and terminology entities in litcovid. the second type is social media data, particularly tweets. covid-19 related tweet datasets are built for many analytic tasks such as identification of informative tweets @xcite , and disinformation detection and fact-checking (shahi and nandini, 2020; @xcite . the most relevant work to ours is proposed by @xcite , that aims to extract covid-19 events reporting test results, death cases, cures and prevention from english tweets. as twitter is rarely used by vietnamese people, we could not use it for data collection. ## conclusion in this paper, we have presented the first manuallyannotated vietnamese dataset in the covid-19 domain, focusing on the named entity recognition task. we empirically conduct experiments on our dataset to compare strong baselines and find that the input representations and the pre-trained language models all have influences on this covid-19 related ner task. we hope that our dataset can serve as the starting point for further vietnamese nlp research and applications in fighting the covid-19 and other future epidemics. here, pat, occ, loc, dat and sym abbreviate patient_id, occupation, location, date and symptom&disease, respectively. recall that an annotation patient_id over "x" refers to as the x th patient having covid-19 in vietnam (e.g. in example 1: "669" refers to as the 669 th patient).