Open Access
Issue |
BIO Web Conf.
Volume 146, 2024
2nd Biology Trunojoyo Madura International Conference (BTMIC 2024)
|
|
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Article Number | 01041 | |
Number of page(s) | 9 | |
Section | Dense Matter | |
DOI | https://doi.org/10.1051/bioconf/202414601041 | |
Published online | 27 November 2024 |
- S. S. Sazali, N. A. Rahman, and Z. A. Bakar, Characteristics of Malay translated hadith corpus, J. King Saud Univ. - Comput. Inf. Sci., 34, 5, 2151–2160, (2022), doi: https://doi.Org/10.1016/j.jksuci.2020.07.011 [Google Scholar]
- A. Z. Ni’mah, Ana Tsalitsatun; Arifin, Perbandingan Metode Term Weighting terhadap Hasil Klasifikasi Teks pada Dataset Terjemahan Kitab Hadis, Rekayasa J. Sci. Technol., 13, 2, 172–180, 2020, doi: https://doi.org/10.21107/rekayasa.v13i2.6412 [Google Scholar]
- A. A. A. Gutub and K. A. Alaseri, Refining Arabic text stego-techniques for shares memorization of counting-based secret sharing, J. King Saud Univ. - Comput. Inf. Sci., 33, 9, 1108–1120, (2021), doi: https://doi.Org/10.1016/j.jksuci.2019.06.014 [Google Scholar]
- H. Maraoui, K. Haddar, and L. Romary, Arabic factoid Question-Answering system for Islamic sciences using normalized corpora,” Procedia Comput. Sci., 192, 69–79, (2021), doi: https://doi.org/10.1016/j.procs.2021.08.008 [Google Scholar]
- N. Aqilah, P. Rostam, N. Hashimah, and A. Hassain, Text categorisation in Quran and Hadith : Overcoming the interrelation challenges using machine learning and term weighting, J. King Saud Univ. - Comput. Inf. Sci., 33, 6, 658–667, (2021), doi: https://doi.org/10.1016/j.jksuci.2019.03.007 [Google Scholar]
- A. B. Kamran, B. Abro, and A. Basharat, SemanticHadith: An ontology-driven knowledge graph for the hadith corpus, J. Web Semant., 78, 100797, (2023), doi: https://doi.org/10.1016/j.websem.2023.100797 [Google Scholar]
- S. Yang, Y. Wang, and X. Chu, A Survey of Deep Learning Techniques for Neural Machine Translation, (2020), https://doi.org/10.48550/arXiv.2002.07526 [Google Scholar]
- D. Licari and G. Comandé, ITALIAN-LEGAL-BERT models for improving natural language processing tasks in the Italian legal domain, Comput. Law Secur. Rev., 52, (2024), doi: https://doi.org/10.1016/j.clsr.2023.105908 [Google Scholar]
- J. Yang et al., BERT and hierarchical cross attention-based question answering over bridge inspection knowledge graph, Expert Syst. Appl., 233, June, 120896, (2023), doi: https://doi.org/10.1016/j.eswa.2023.120896 [Google Scholar]
- F. Y. Azalia, M. A. Bijaksana, and A. F. Huda, Name indexing in Indonesian translation of hadith using named entity recognition with naïve bayes classifier, Procedia Comput. Sci., 157, 142–149, (2019), doi: https://doi.org/10.1016/j.procs.2019.08.151 [Google Scholar]
- S. I. Melia et al., The Ngoko Javanese Stemmer uses the Enhanced Confix Stripping Stemmer Method, J. Comput. Sci. Informatics Eng., 16, 1, 107–112, (2023), doi: https://doi.org/10.29303/jcosine.v6i2.471 [Google Scholar]
- A. T. Ni’mah and F. Syuhada, Term Weighting Based Indexing Class and Indexing Short Document for Indonesian Thesis Title Classification, J. Comput. Sci. Informatics Eng., 6, 2, 167–175, (2022), doi: https://doi.org/10.29303/jcosine.v6i2.471 [Google Scholar]
- A. I. Al-Ghadir, A. M. Azmi, and A. Hussain, A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments,” Inf. Fusion, 67, 29–40, (2021), doi: https://doi.org/10.1016/j.inffus.2020.10.003 [CrossRef] [Google Scholar]
- R. Wongso, F. A. Luwinda, B. C. Trisnajaya, O. Rusli, and Rudy, News Article Text Classification in Indonesian Language, Procedia Comput. Sci., 116, 137–143, (2017), doi: https://doi.Org/10.1016/j.procs.2017.10.039 [Google Scholar]
- Y. Guo et al., ESIE-BERT: Enriching sub-words information explicitly with BERT for intent classification and slot filling, Neurocomputing, 591, April, 127725, (2024), doi: https://doi.org/10.1016/j.neucom.2024.127725 [CrossRef] [Google Scholar]
- R. H. AlMahmoud and M. Alian, The effect of clustering algorithms on question answering,” Expert Syst. Appl., 243, November, 122959, (2024), doi: https://doi.org/10.1016/j.eswa.2023.122959 [Google Scholar]
- B. A. Benali, S. Mihi, N. Laachfoubi, and A. A. Mlouk, Arabic Named Entity Recognition in Arabic Tweets Using BERT-based Models,” Procedia Comput. Sci., 203, 733–738, (2022), doi: https://doi.org/10.1016/j.procs.2022.07.109 [Google Scholar]
- X. Chen, B. He, K. Hui, L. Sun, and Y. Sun, Dealing with textual noise for robust and effective BERT re-ranking, Inf. Process. Manag., 60, 1, 103135, (2023), doi: https://doi.org/10.1016/j.ipm.2022.103135 [Google Scholar]
- D. Lin, J. Tang, X. Li, K. Pang, S. Li, and T. Wang, BERT-SMAP: Paying attention to Essential Terms in passage ranking beyond BERT, Inf. Process. Manag., 59, 2, 102788, (2022), doi: https://doi.org/10.1016/j.ipm.2021.102788 [Google Scholar]
- J. Briskilal and C. N. Subalalitha, An ensemble model for classifying idioms and literal texts using BERT and RoBERTa, Inf. Process. Manag., 59, 1, 102756, (2022), doi: https://doi.org/10.1016/j.ipm.2021.102756 [Google Scholar]
- S. Abarna, J. I. Sheeba, and S. P. Devaneyan, An ensemble model for idioms and literal text classification using knowledge-enabled BERT in deep learning, Meas. Sensors, 24, August, 100434, (2022), doi: https://doi.org/10.1016/j.measen.2022.100434 [CrossRef] [Google Scholar]
- H. Jarquín-Vásquez, H. J. Escalante, and M. Montes-y-Gómez, Enhancing abusive language detection: A domain-adapted approach leveraging BERT pre-training tasks, Pattern Recognit. Lett., November, (2024), doi: https://doi.org/10.1016/j.patrec.2024.05.007 [Google Scholar]
- S. Ji, M. Holtta. and P. Marttinen, Does the magic of BERT apply to medical code assignment? A quantitative study,” Comput. Biol. Med., 139, October, (2021), doi: https://doi.org/10.1016/j.compbiomed.2021.104998 [Google Scholar]
- A. Qazi and R. H. Goudar, ScienceDirect An Ontology-based Term Weighting Technique for Web Document Categorization,” Procedia Comput. Sci., 133, 75–81, (2018), doi: https://doi.org/10.1016/j.procs.2018.07.010 [Google Scholar]
- S. Jamshidi et al., Effective text classification using BERT, MTM LSTM, and DT, Data Knowl. Eng., 151, August,. 102306, (2024), doi: https://doi.org/10.1016/j.datak.2024.102306 [Google Scholar]
- K. Kaur and P. Kaur, Improving BERT model for requirements classification by bidirectional LSTM-CNN deep model,” Comput. Electr. Eng., 108, January, 108699, (2023), doi: https://doi.Org/10.1016/j.compeleceng.2023.108699 [Google Scholar]
- A. T. Ni’mah, F. Solihin, and I. U. Sari, Blended learning implementation of outcome-based education curriculum in learning management systems, E3S Web Conf., 499, 1–9, (2024), doi: https://doi.org/10.1051/e3sconf/202449901020 [Google Scholar]
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