An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data
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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/8/740 |
| _version_ | 1797585559119462400 |
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| author | Liang-Ching Chen Kuei-Hu Chang |
| author_facet | Liang-Ching Chen Kuei-Hu Chang |
| author_sort | Liang-Ching Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:07:51Z |
| format | Article |
| id | doaj.art-caa8550c5c2b4be38f52be55e7a30fb4 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T00:07:51Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-caa8550c5c2b4be38f52be55e7a30fb42023-11-19T00:14:30ZengMDPI AGAxioms2075-16802023-07-0112874010.3390/axioms12080740An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus DataLiang-Ching Chen0Kuei-Hu Chang1Department of Foreign Languages, R.O.C. Military Academy, Kaohsiung 830, TaiwanDepartment of Management Sciences, R.O.C. Military Academy, Kaohsiung 830, TaiwanThe 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.https://www.mdpi.com/2075-1680/12/8/740corpus assessment approachnatural language processing (NLP)COVID-19analytic hierarchy process (AHP)environmental science |
| spellingShingle | Liang-Ching Chen Kuei-Hu Chang An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data Axioms corpus assessment approach natural language processing (NLP) COVID-19 analytic hierarchy process (AHP) environmental science |
| title | An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data |
| title_full | An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data |
| title_fullStr | An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data |
| title_full_unstemmed | An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data |
| title_short | An Extended AHP-Based Corpus Assessment Approach for Handling Keyword Ranking of NLP: An Example of COVID-19 Corpus Data |
| title_sort | extended ahp based corpus assessment approach for handling keyword ranking of nlp an example of covid 19 corpus data |
| topic | corpus assessment approach natural language processing (NLP) COVID-19 analytic hierarchy process (AHP) environmental science |
| url | https://www.mdpi.com/2075-1680/12/8/740 |
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