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...

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Bibliographic Details
Published in:International Journal of Intelligent Systems 2021-07, Vol.36 (7), p.3190-3216
Main Authors: Chen, Liang‐Ching, Chang, Kuei‐Hu
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
corpus
COVID-19
Criteria
Data processing
Dispersion
Handling
Hirsch index
H‐index algorithm
Intelligent systems
Machine learning
Medical personnel
Ranking
Words (language)
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Items that cite this one
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Summary: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.
ISSN:0884-8173
1098-111X
1098-111X
DOI:10.1002/int.22413