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

Chen, Liang-Ching; Chang, Kuei-Hu

doi:10.1002/int.22413

Cited by 12 publications

(4 citation statements)

References 48 publications

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“…To enhance the accuracy of the corpus evaluation results, a corpus assessment approach must be able to compute multiple parameters at the same time and consider the relative importance between different parameters. However, the traditional keyword-list-generating approach [10] only uses the likelihood ratio method [11] to determine and rank keywords in the target corpus, which is a deficiency of corpus assessment [2,3,10,22]. Thus, to optimize the aforementioned issues, this paper proposed an extended AHP-based corpus assessment approach that integrated the likelihood ratio method, the corpus optimization approach, and the AHP method to refine corpus data, simultaneously handle multiple parameters, and consider the relative importance between different parameters for accurately evaluating keywords.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Corpus assessment approaches have been widely used as an NLP tool in the fields of social sciences and the sciences to explore the linguistic patterns of specific domains [1][2][3]10,[21][22][23]. The traditional keyword-list-generating approach [10] is based on the likelihood ratio method, which is an IR approach utilized in many types of corpus software [1,23] to calculate a token's keyness value and rank tokens to form a keyword list.…”

Section: Introductionmentioning

confidence: 99%

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An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data

Chen

Chang

2023

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The use of corpus assessment approaches to determine and rank keywords for corpus data is critical due to the issues of information retrieval (IR) in Natural Language Processing (NLP), such as when encountering COVID-19, as it can determine whether people can rapidly obtain knowledge of the disease. The algorithms used for corpus assessment have to consider multiple parameters and integrate individuals’ subjective evaluation information simultaneously to meet real-world needs. However, traditional keyword-list-generating approaches are based on only one parameter (i.e., the keyness value) to determine and rank keywords, which is insufficient. To improve the evaluation benefit of the traditional keyword-list-generating approach, this paper proposed an extended analytic hierarchy process (AHP)-based corpus assessment approach to, firstly, refine the corpus data and then use the AHP method to compute the relative weights of three parameters (keyness, frequency, and range). To verify the proposed approach, this paper adopted 53 COVID-19-related research environmental science research articles from the Web of Science (WOS) as an empirical example. After comparing with the traditional keyword-list-generating approach and the equal weights (EW) method, the significant contributions are: (1) using the machine-based technique to remove function and meaningless words for optimizing the corpus data; (2) being able to consider multiple parameters simultaneously; and (3) being able to integrate the experts’ evaluation results to determine the relative weights of the parameters.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data

Chen

Chang

2023

Axioms

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“…In the work by Chen and Chang [13], they developed 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 work compiled 100 Covid-19-related research articles as an empirical example of the target corpus.…”

Section: Text Corpora To Face Problems Related To Covid-19mentioning

confidence: 99%

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

Álvarez‐Carmona

Aranda

Rodríguez‐González

et al. 2022

Journal of Information Science

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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.

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“…In natural language processing, novel machine learning algorithms must be designed to address issues such as the structure and semantics of text data. Thus, the development of information processing is essential in promoting the continuous evolution of AI technology [32][33][34][35][36]. Through evaluations and analyses, this study could obtain the growth trends and mainstream publications in the field of AI and information processing and define scholars who have made significant contributions-along with their countries and organizations, as well as their collaborative networks-to understand the development trajectory and trends.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Information Processing: A Systematic Literature Review

Lin

Chang

2023

Mathematics

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This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including “Deep learning”, “Machine learning”, and “Feature extraction” are components that play a crucial role. Additionally, the most representative research areas were “Engineering”, “Operations Research and Management Science”, and “Automation Control Systems”. Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing.

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

Cited by 12 publications

References 48 publications

An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data

An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data

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

Artificial Intelligence and Information Processing: A Systematic Literature Review

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