COVID-19 Mythbusters in World Languages
This paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original texts are published by World Health Organization (WHO). This paper then presents preliminary analyses on latin-alphabet-based texts to see the potential of the database as a resource for multilingual linguistic analyses. The analyses on latin-alphabet-based texts gave interesting insights into the resource. While the amount of translated texts in each language was small, character bi-grams with normalization (lowercasing and removal of diacritics) was turned out to be an effective proxy for measuring the similarity of the languages, and the affinity ranking of language pairs could be obtained. Additionally, the hierarchical clustering analysis is performed using the character bigram overlap ratio of every possible pair of languages. The result shows the cluster of Germanic languages, Romance languages, and Southern Bantu languages. In sum, the multilingual database not only offers fixed set of materials in numerous languages, but also serves as a preliminary tool to identify the language family using text-based similarity measure of bigram overlap ratio.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
TICO-19: the Translation Initiative for Covid-19
The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect thems…
TranslationA System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very differ…
ArticlesMachine TranslationTranslationIRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages
Emerged in Wuhan city of China in December 2019, COVID-19 continues to spread rapidly across the world despite authorities having made available a number of vaccines. While the coronavirus has been around for a significa…
CovTiNet: Covid text identification network using attention-based positional embedding feature fusion
Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding a large volume of Covid-affiliated text on the World Wide Web due …
MisinformationSenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases …
Language ModellingSentiment Analysis