Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis
Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33202/ http://umpir.ump.edu.my/id/eprint/33202/1/Study%20of%20keyword%20extraction%20techniques%20for%20electric%20double-layer%20capacitor%20domain.pdf |
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| author | Miah, M. Saef Ullah Junaida, Sulaiman Talha, Sarwar Kamal Z., Zamli Jose, Rajan |
| author_facet | Miah, M. Saef Ullah Junaida, Sulaiman Talha, Sarwar Kamal Z., Zamli Jose, Rajan |
| author_sort | Miah, M. Saef Ullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques. |
| first_indexed | 2025-11-15T03:09:14Z |
| format | Article |
| id | ump-33202 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:09:14Z |
| publishDate | 2021 |
| publisher | Hindawi Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-332022022-01-13T03:17:37Z http://umpir.ump.edu.my/id/eprint/33202/ Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis Miah, M. Saef Ullah Junaida, Sulaiman Talha, Sarwar Kamal Z., Zamli Jose, Rajan QA76 Computer software QD Chemistry T Technology (General) Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques. Hindawi Limited 2021-12-02 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/33202/1/Study%20of%20keyword%20extraction%20techniques%20for%20electric%20double-layer%20capacitor%20domain.pdf Miah, M. Saef Ullah and Junaida, Sulaiman and Talha, Sarwar and Kamal Z., Zamli and Jose, Rajan (2021) Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis. Complexity, 2021 (8192320). pp. 1-12. ISSN 1076-2787. (Published) https://doi.org/10.1155/2021/8192320 https://doi.org/10.1155/2021/8192320 |
| spellingShingle | QA76 Computer software QD Chemistry T Technology (General) Miah, M. Saef Ullah Junaida, Sulaiman Talha, Sarwar Kamal Z., Zamli Jose, Rajan Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title | Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title_full | Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title_fullStr | Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title_full_unstemmed | Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title_short | Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis |
| title_sort | study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: an experimental analysis |
| topic | QA76 Computer software QD Chemistry T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/33202/ http://umpir.ump.edu.my/id/eprint/33202/ http://umpir.ump.edu.my/id/eprint/33202/ http://umpir.ump.edu.my/id/eprint/33202/1/Study%20of%20keyword%20extraction%20techniques%20for%20electric%20double-layer%20capacitor%20domain.pdf |