A Novel Statistic-Based Corpus Machine Processing Approach to Refine a Big Textual Data: An ESP Case of COVID-19 News Reports

A Semi-automated Approach for Bengali Neologism

Neologisms refer to newly coined words or phrases adopted by a language, and it is a slow but ongoing process that occurs in all languages. Sometimes, rarely used or obsolete words are also considered neologisms. Certain events, such as wars, the emergence of new diseases, or advancements like computers and the internet, can trigger the creation of new words or neologisms. The COVID-19 pandemic is one such event that has rapidly led to an explosion of neologisms in the context of the disease and several other social contexts. Even the term COVID-19 itself is a newly coined term. Studying such adaptation or change and quantifying it is essential from a linguistic perspective. However, identifying newly coined terms or extracting neologisms computationally is a challenging task. The standard tools and techniques for finding newly coined terms in English-like languages may not be suitable for Bengali and other Indic languages. This study aims to use a semi-automated approach to investigate the emergence or modification of new words in the Bengali language amidst the COVID-19 pandemic. To conduct this study, a Bengali web corpus was compiled consisting of COVID-19 related articles sourced from various web sources in Bengali. The current experiment focuses solely on COVID-19-related neologisms, but the method can be adapted for general purposes and extended to other languages as well.

Classifying the Mexican epidemiological semaphore colour from the Covid-19 text Spanish news

This work aims to generate classification models that help determine the colour of an epidemiological semaphore (ES) by analysing online news and being better prepared for the different changes in the evolution of the pandemic. To accomplish this, we introduce Cov-NES-Mex corpus, a collection of 77,983 news (labelled with the Mexican ES system) related to Covid-19 for the 32 regions of Mexico. Also, we showed measures that describe the corpus as imbalanced and with a high vocabulary overlap between classes. In addition, evaluation measurements of the pandemic by region are proposed. Furthermore, a classification model, based on a transformer architecture specialised for the Spanish language, achieved up to 0.83 of F-measure. Thus, this work provides evidence that there is essential information in the news that can be used to determine the colour of the ES up to 4 weeks in advance. Finally, the presented results could be applied to other Spanish-speaking countries, which do not have an ES system, thus inferring and comparing their situation concerning the Mexican ES.

Big Data Research in Fighting COVID-19: Contributions and Techniques

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

A novel corpus-based computing method for handling critical word-ranking issues: An example of COVID-19 research articles

A corpus is a massive body of structured textual data that are stored and operated electronically. It usually combines with statistics, machine learning algorithms, or artificial intelligence (AI) technologies to explore the semantic relationship between lexical units, and beneficial when applied to language learning, information processing, translation, and so forth. In the face of a novel disease, like, COVID-19, establishing medical-specific corpus will enhance frontline medical personnel's information acquisition efficiency, guiding them on the right approaches to respond to and prevent the novel disease. To effectively retrieve critical messages from the corpus, appropriately handling word-ranking issues is quite crucial. However, traditional frequency-based approaches may cause bias in handling word-ranking issues because they neither optimize the corpus nor integrally take words' frequency dispersion and concentration criteria into consideration. Thus, this paper develops a novel corpus-based approach that combines a corpus software and Hirsch index (H-index) algorithm to handle the aforementioned issues simultaneously, making word-ranking processes more accurate. This paper compiled 100 COVID-19-related research articles as an empirical example of the target corpus. To verify the proposed approach, this study compared the results of two traditional frequency-based approaches and the proposed approach. The results indicate that the proposed approach can refine corpus and simultaneously compute words' frequency dispersion and concentration criteria in handling word-ranking issues.