IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran

R.H. Gusmita, A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Natural Language Processing and Information Systems, Springer Nature Switzerland, Cham, 2023.

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Book Chapter | Published | English
Abstract
Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range.
Publishing Year
Book Title
Natural Language Processing and Information Systems
Conference
International Conference on Applications of Natural Language to Information Systems (NLDB) 2023
Conference Location
Derby, UK
Conference Date
2023-06-21 – 2023-06-23
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Cite this

Gusmita RH, Firmansyah AF, Moussallem D, Ngonga Ngomo A-C. IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran. In: Natural Language Processing and Information Systems. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-35320-8_12
Gusmita, R. H., Firmansyah, A. F., Moussallem, D., & Ngonga Ngomo, A.-C. (2023). IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran. In Natural Language Processing and Information Systems. International Conference on Applications of Natural Language to Information Systems (NLDB) 2023, Derby, UK. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35320-8_12
@inbook{Gusmita_Firmansyah_Moussallem_Ngonga Ngomo_2023, place={Cham}, title={IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran}, DOI={10.1007/978-3-031-35320-8_12}, booktitle={Natural Language Processing and Information Systems}, publisher={Springer Nature Switzerland}, author={Gusmita, Ria Hari and Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2023} }
Gusmita, Ria Hari, Asep Fajar Firmansyah, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran.” In Natural Language Processing and Information Systems. Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-35320-8_12.
R. H. Gusmita, A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran,” in Natural Language Processing and Information Systems, Cham: Springer Nature Switzerland, 2023.
Gusmita, Ria Hari, et al. “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran.” Natural Language Processing and Information Systems, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-35320-8_12.

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