SlowGuess/NAACL_2021_OCR huggingface dataset:
Gautam Kishore Shahi and Durgesh Nandini. 2020. FakeCovid - A Multilingual Cross-domain Fact Check News Dataset for COVID-19. In Proceedings of the International Workshop on Cyber Social Threats.
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham Barezi, and Pascale Fung. 2020. CAIRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020.
Andrew Trotman, Antti Puurula, and Blake Burgess. 2014. Improvements to BM25 and language models examined. In Proceedings of the 2014 Australasian Document Computing Symposium, pages 58-65.
Karin Verspoor, Kevin Bretonnel Cohen, Arrick Lanfranchi, Colin Warner, Helen L. Johnson, Christophe Roeder, Jinho D. Choi, Christopher S. Funk, Yuriy Malenkiy, Miriam Eckert, Nianwen Xue, William A. Baumgartner Jr., Michael Bada, Martha Palmer, and Lawrence E. Hunter. 2012. A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools. BMC Bioinform., 13:207.
Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, and David Martinez. 2020. COVID-SEE: Scientific Evidence Explorer for COVID-19 related research. arXiv preprint arXiv:2008.07880.
Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen, Mark Dras, and Mark Johnson. 2018. VnCoreNLP: A Vietnamese Natural Language Processing Toolkit. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 56-60.
Lucy Lu Wang, Kyle Lo, et al. 2020. CORD-19: The COVID-19 Open Research Dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020.
Thomas Wolf, Lysandre Debut, et al. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45.
Edwin Zhang, Nikhil Gupta, Raphael Tang, Xiao Han, Ronak Pradeep, Kuang Lu, Yue Zhang, Rodrigo Nogueira, Kyunghyun Cho, Hui Fang, et al. 2020. Covidex: Neural ranking models and keyword search infrastructure for the Covid-19 open research dataset. arXiv preprint, arXiv:2007.07846.
Shi Zong, Ashutosh Baheti, Wei Xu, and Alan Ritter. 2020. Extracting COVID-19 Events from Twitter. arXiv preprint, arXiv:2006.02567.
Annotation examples
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  $X^{\text{th}}$  patient having COVID-19 in Vietnam (e.g. in Example 1: "669" refers to as the  $669^{\text{th}}$  patient).
Notices over entity types
We have two principles for selecting the ten entity types: (i) Entities should contain key information related to the COVID-19 patients (here, the information should be helpful in the context of contact tracing and monitoring the growth of the pandemic); and (ii) The availability of entity types in the text, i.e., how frequent does each of the entity types appear. This is decided based on manual observations of news articles.
In the context of contact tracing, it is more useful to broaden the scope of location. For example, when a patient is presented at an organization, we refer to that organization as a location if we can infer its specific location on the map. In Example 1, we would label the entity mention “Bêngh viên Dà khoa Dong Nai” (Dong Nai General Hospital) with
LOCATION as its provide information about the place that a patient used to be at. On the other hand, in Example 2, the entity mention "Bên h身亡 Bên Nhiêt doit TP HCM" (Ho Chi Minh City Hospital for Tropical Diseases) is labeled as ORGANIZATION because it acts as the subject executing a specific action (i.e. reporting a test result).
For OCCUPATION, AGE and GENDER entities, we only tag them if we can link the corresponding entity mentions to a specific entity with NAME or PATIENT_ID label within the same sentence. In Example 1, "bác si" (doctor) is the occupation of patient "669", thus we label this mention as an entity of type OCCUPATION. However, in Example 3, we do not label "dieu duong" (nurses) as OCCUPATION as we cannot link this mention to any determined person.
For SYMPTOM&DISEASE entities, we prefer the entities to be as detailed as possible. For instance, in Example 4 we consider words denoting the levels of severity as part of diseases, such as "nǎng" (severe), "khòng hòi phuc" (unrecoverable), "giai doàn cuǒi" (terminal) and "man (chronic).