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BERT based named entity recognition for automated hadith narrator identification

Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challe...

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Main Authors: Luthfi, Emha Taufiq, Mohd Yusoh, Zeratul Izzah, Mohd Aboobaider, Burhanuddin
Format: Article
Language:English
Published: Science and Information Organization 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26420/2/2022%20EMHA%20IJACSA.PDF
http://eprints.utem.edu.my/id/eprint/26420/
https://thesai.org/Downloads/Volume13No1/Paper_73-BERT_based_Named_Entity_Recognition.pdf
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spelling my.utem.eprints.264202023-03-28T13:44:43Z http://eprints.utem.edu.my/id/eprint/26420/ BERT based named entity recognition for automated hadith narrator identification Luthfi, Emha Taufiq Mohd Yusoh, Zeratul Izzah Mohd Aboobaider, Burhanuddin Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths. In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn. Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith. The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith. The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model. In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent. On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score. Science and Information Organization 2022 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26420/2/2022%20EMHA%20IJACSA.PDF Luthfi, Emha Taufiq and Mohd Yusoh, Zeratul Izzah and Mohd Aboobaider, Burhanuddin (2022) BERT based named entity recognition for automated hadith narrator identification. International Journal of Advanced Computer Science and Applications, 13 (1). pp. 604-611. ISSN 2158-107X https://thesai.org/Downloads/Volume13No1/Paper_73-BERT_based_Named_Entity_Recognition.pdf 10.14569/IJACSA.2022.0130173
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths. In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn. Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith. The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith. The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model. In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent. On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score.
format Article
author Luthfi, Emha Taufiq
Mohd Yusoh, Zeratul Izzah
Mohd Aboobaider, Burhanuddin
spellingShingle Luthfi, Emha Taufiq
Mohd Yusoh, Zeratul Izzah
Mohd Aboobaider, Burhanuddin
BERT based named entity recognition for automated hadith narrator identification
author_facet Luthfi, Emha Taufiq
Mohd Yusoh, Zeratul Izzah
Mohd Aboobaider, Burhanuddin
author_sort Luthfi, Emha Taufiq
title BERT based named entity recognition for automated hadith narrator identification
title_short BERT based named entity recognition for automated hadith narrator identification
title_full BERT based named entity recognition for automated hadith narrator identification
title_fullStr BERT based named entity recognition for automated hadith narrator identification
title_full_unstemmed BERT based named entity recognition for automated hadith narrator identification
title_sort bert based named entity recognition for automated hadith narrator identification
publisher Science and Information Organization
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26420/2/2022%20EMHA%20IJACSA.PDF
http://eprints.utem.edu.my/id/eprint/26420/
https://thesai.org/Downloads/Volume13No1/Paper_73-BERT_based_Named_Entity_Recognition.pdf
_version_ 1761623119262384128
score 13.211869

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