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

Abstract

With developments of modern and advanced information and communication technologies (ICTs), Industry 4.0 has launched big data analysis, natural language processing (NLP), and artificial intelligence (AI). Corpus analysis is also a part of big data analysis. For many cases of statistic-based corpus techniques adopted to analyze English for specific purposes (ESP), researchers extracted critical information by retrieving domain-oriented lexical units. However, even if corpus software embraces algorithms such as log-likelihood tests, log ratios, BIC scores, etc., the machine still cannot understand linguistic meanings. In many ESP cases, function words reduce the efficiency of corpus analysis. However, many studies still use manual approaches to eliminate function words. Manual annotation is inefficient and time-wasting, and can easily cause information distortion. To enhance the efficiency of big textual data analysis, this paper proposes a novel statistic-based corpus machine processing approach to refine big textual data. Furthermore, this paper uses COVID-19 news reports as a simulation example of big textual data and applies it to verify the efficacy of the machine optimizing process. The refined resulting data shows that the proposed approach is able to rapidly remove function and meaningless words by machine processing and provide decision-makers with domain-specific corpus data for further purposes.

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

“…When competitor methods (i.e., the traditional frequency-based approach 30 and the refined traditional frequency-based approach 46 ) in handling word-ranking issues only based on words' frequency values or range values, respectively, to determine their sequences, namely, traditional methods do not integrally take a word's dispersion and concentration criteria into account. This deficiency will cause critical word-ranking results exist bias, in addition, the importance levels of high-frequency critical words will be challenged.…”

“…The traditional frequency-based approach 30 The refined traditional frequency-based approach 44 The proposed approach According to Table 8, there were significant differences in token ranking between the traditional corpus-based computing approaches 30,46 and the proposed approach. The traditional corpus-based computing approaches 30,46 only calculated a token's total frequency values to define its rank and importance; however, the frequency dispersion criteria were not taken into consideration; that is, a token with high frequency may not be widely adopted or used by the RA authors, or may be concentrated in very few RAs or even possibly occur in only one RA. Nevertheless, the proposed approach not only used H-index to compute the dispersion and concentration criteria of frequency simultaneously, but also used frequency values to distinguish tokens that had the same H-index values.…”

“…The calculation results of the proposed approach redefine the importance of tokens (N = 420) that were compared with the traditional corpus-based computing approaches. 30,46 In other words, the authors found only 11 tokens (2.6%) that remained at original ranks and only nine tokens (2.1%) among them in the top 50 wordlists (see Table 8), 15 tokens (3.5%) that moved forward more than 100 ranks, respectively, 196 tokens (46.6%) that moved forward from 1 to 99 ranks, respectively, 14 tokens (3.3%) that moved backward more than 100 ranks, respectively, and 184 tokens (43.8%) that moved backward from 1 to 99 ranks, respectively. In other words, the proposed approach successfully re-evaluates the importance of tokens and makes more than 97% changes by adopting H-index algorithm which simultaneously took the dispersion and concentration criteria of frequency into consideration (see Table 9).…”

Cognitive Metaphors of Covid-19 Pandemic in Business News

“…The studies with a focus on applied linguistics perspective were carried out to discover and explain new coinage related to Covid-19 situation, its influence on other languages and problems arising in the translation and coordination of terminology [9][10][11], generate taxonomies of terms with the help of corpus analysis and estimate word frequencies [12][13][14], collect and systematize massive Covid-19 related text data [15]. The findings shed light on the specificity of scientific and medical language which is significant in specialist and everyday discourse.…”

“…According to Haddad & Monterero-Martinez [9], a tremendous vocabulary expansion should be attributed to solving communication needs in specialist and everyday communication by filling lexical gaps. The data from various studies [9][10][11][12][13][14][15] indicated that most productive types of vocabulary development included metaphoric and metonymic transfers. Affixation, compounding, abbreviation, clipping and conversion prevailed over other word-building processes.…”