WikiANN, also known as PAN-X, is a multilingual named entity recognition dataset. It consists of Wikipedia articles that have been annotated with LOC (location), PER (person), and ORG (organization) tags in the IOB2 format¹². This dataset serves as a valuable resource for training and evaluating named entity recognition models across various languages.
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PhoNER_COVID19 is a dataset for recognising COVID-19 related named entities in Vietnamese, consisting of 35K entities over 10K sentences. The authors defined 10 entity types with the aim of extracting key information related to COVID-19 patients, which are especially useful in downstream applications. In general, these entity types can be used in the context of not only the COVID-19 pandemic but also in other future epidemics.
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ViMQ is a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and Named Entity Recognition tasks. It contains Vietnamese medical questions crawled from the consultation section online between patients and doctors from www.vinmec.com, a website of a Vietnamese general hospital. Each consultation consists of a question regarding a specific health issue of a patient and a detailed respond provided by a clinical expert. The dataset contains health issues that fall into a wide range of categories including common illness, cardiology, hematology, cancer, pediatrics, etc. We removed sections where users ask about information of the hospital and selected 9,000 questions for the dataset.
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