Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach
The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a h...
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| Format: | Article |
| Language: | English |
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The Science and Information (SAI) Organization Limited
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33320/1/Keyphrases%20Concentrated%20Area%20Identification.pdf |
| _version_ | 1796994948819582976 |
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| author | Miah, Mohammad Badrul Alam Suryanti, Awang Md Saiful, Azad Rahman, Md Mustafizur |
| author_facet | Miah, Mohammad Badrul Alam Suryanti, Awang Md Saiful, Azad Rahman, Md Mustafizur |
| author_sort | Miah, Mohammad Badrul Alam |
| collection | UMP |
| description | The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains. |
| first_indexed | 2024-03-06T12:55:07Z |
| format | Article |
| id | UMPir33320 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:07Z |
| publishDate | 2022 |
| publisher | The Science and Information (SAI) Organization Limited |
| record_format | dspace |
| spelling | UMPir333202024-01-05T01:56:01Z http://umpir.ump.edu.my/id/eprint/33320/ Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach Miah, Mohammad Badrul Alam Suryanti, Awang Md Saiful, Azad Rahman, Md Mustafizur QA75 Electronic computers. Computer science The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains. The Science and Information (SAI) Organization Limited 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/33320/1/Keyphrases%20Concentrated%20Area%20Identification.pdf Miah, Mohammad Badrul Alam and Suryanti, Awang and Md Saiful, Azad and Rahman, Md Mustafizur (2022) Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach. International Journal of Advanced Computer Science and Applications (IJACSA), 13 (1). pp. 788-796. ISSN 2156-5570(Online). (Published) https://thesai.org/Downloads/Volume13No1/Paper_92-Keyphrases_Concentrated_Area_Identification_from_Academic_Articles.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Miah, Mohammad Badrul Alam Suryanti, Awang Md Saiful, Azad Rahman, Md Mustafizur Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title | Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title_full | Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title_fullStr | Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title_full_unstemmed | Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title_short | Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach |
| title_sort | keyphrases concentrated area identification from academic articles as feature of keyphrase extraction a new unsupervised approach |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/33320/1/Keyphrases%20Concentrated%20Area%20Identification.pdf |
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