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16 results for “Quran”
IndQNER: Indonesian Benchmark Dataset from the Indonesian Translation of the Quran
<h2>IndQNER</h2> <p>IndQNER is a Named Entity Recognition (NER) benchmark dataset that was created by manually annotating 8 chapters in the Indonesian translation of the Quran. The annotation was performed using a web-based text annotation tool, <a href="https://www.tagtog.com/" target="_blank" rel="noopener">Tagtog</a>, and the BIO (Beginning-Inside-Outside) tagging format. The dataset contains:</p> <ul> <li>3117 sentences</li> <li>62027 tokens</li> <li>2475 named entities</li> <li>18 named entity categories</li> </ul> <h2>Named Entity Classes</h2> <p>The named entity classes were initially defined by analyzing the existing Quran concepts ontology. The initial classes were updated based on the information acquired during the annotation process. Finally, there are 20 classes, as follows:</p> <ol> <li>Allah</li> <li>Allah's Throne</li> <li>Artifact</li> <li>Astronomical body</li> <li>Event</li> <li>False deity</li> <li>Holy book</li> <li>Language</li> <li>Angel</li> <li>Person</li> <li>Messenger</li> <li>Prophet</li> <li>Sentient</li> <li>Afterlife location</li> <li>Geographical location</li> <li>Color</li> <li>Religion</li> <li>Food</li> <li>Fruit</li> <li>The book of Allah</li> </ol> <h2>Annotation Stage</h2> <p>There were eight annotators who contributed to the annotation process. They were informatics engineering students at the State Islamic University Syarif Hidayatullah Jakarta.</p> <ol> <li>Anggita Maharani Gumay Putri</li> <li>Muhammad Destamal Junas</li> <li>Naufaldi Hafidhigbal</li> <li>Nur Kholis Azzam Ubaidillah</li> <li>Puspitasari</li> <li>Septiany Nur Anggita</li> <li>Wilda Nurjannah</li> <li>William Santoso</li> </ol> <h2>Verification Stage</h2> <p>We found many named entity and class candidates during the annotation stage. To verify the candidates, we consulted Quran and Tafseer (content) experts who are lecturers at Quran and Tafseer Department at the State Islamic University Syarif Hidayatullah Jakarta.</p> <ol> <li>Dr. Eva Nugraha, M.Ag.</li> <li>Dr. Jauhar Azizy, MA</li> <li>Dr. Lilik Ummi Kultsum, MA</li> </ol> <h2>Evaluation</h2> <p>We evaluated the annotation quality of IndQNER by performing experiments in two settings: supervised learning (BiLSTM+CRF) and transfer learning (<a href="https://huggingface.co/indobenchmark/indobert-base-p1" target="_blank" rel="noopener">IndoBERT</a> fine-tuning).</p> <h3>Supervised Learning Setting</h3> <p>The implementation of BiLSTM and CRF utilized <a href="https://huggingface.co/indobenchmark/indobert-base-p1" target="_blank" rel="noopener">IndoBERT</a> to provide word embeddings. All experiments used a batch size of 16. These are the results:</p> <table> <tbody> <tr> <td>Maximum sequence length</td> <td>Number of e-poch</td> <td>Precision</td> <td>Recall</td> <td>F1 score</td> </tr> <tr> <td>256</td> <td>10</td> <td>0.94</td> <td>0.92</td> <td>0.93</td> </tr> <tr> <td>256</td> <td>20</td> <td> 0.99</td> <td>0.97</td> <td>0.98</td> </tr> <tr> <td>256</td> <td>40</td> <td>0.96</td> <td>0.96</td> <td>0.96</td> </tr> <tr> <td>256</td> <td>100</td> <td>0.97</td> <td>0.96</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>10</td> <td>0.92</td> <td>0.92</td> <td>0.92</td> </tr> <tr> <td>512</td> <td>20</td> <td>0.96</td> <td>0.95</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>40</td> <td>0.97</td> <td>0.95</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>100</td> <td>0.97</td> <td>0.95</td> <td>0.96</td> </tr> </tbody> </table> <h3>Transfer Learning Setting</h3> <p>We performed several experiments with different parameters in IndoBERT fine-tuning. All experiments used a learning rate of 2e-5 and a batch size of 16. These are the results:</p> <table> <tbody> <tr> <td>Maximum sequence length</td> <td>Number of e-poch</td> <td>Precision</td> <td>Recall</td> <td>F1 score</td> </tr> <tr> <td>256</td> <td>10</td> <td>0.67</td> <td>0.65</td> <td>0.65</td> </tr> <tr> <td>256</td> <td>20</td> <td> 0.60</td> <td>0.59</td> <td>0.59</td> </tr> <tr> <td>256</td> <td>40</td> <td>0.75</td> <td>0.72</td> <td>0.71</td> </tr> <tr> <td>256</td> <td>100</td> <td>0.73</td> <td>0.68</td> <td>0.68</td> </tr> <tr> <td>512</td> <td>10</td> <td>0.72</td> <td>0.62</td> <td>0.64</td> </tr> <tr> <td>512</td> <td>20</td> <td>0.62</td> <td>0.57</td> <td>0.58</td> </tr> <tr> <td>512</td> <td>40</td> <td>0.72</td> <td>0.66</td> <td>0.67</td> </tr> <tr> <td>512</td> <td>100</td> <td>0.68</td> <td>0.68</td> <td>0.67</td> </tr> </tbody> </table> <p> </p> <p>This dataset is also part of the <a href="https://github.com/IndoNLP/nusa-crowd" target="_blank" rel="noopener">NusaCrowd project</a> which aims to collect Natural Language Processing (NLP) datasets for Indonesian and its local languages.</p> <h2>How to Cite</h2> <p>@InProceedings{10.1007/978-3-031-35320-8_12,<br>author="Gusmita, Ria Hari<br>and Firmansyah, Asep Fajar<br>and Moussallem, Diego<br>and Ngonga Ngomo, Axel-Cyrille",<br>editor="M{\'e}tais, Elisabeth<br>and Meziane, Farid<br>and Sugumaran, Vijayan<br>and Manning, Warren<br>and Reiff-Marganiec, Stephan",<br>title="IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran",<br>booktitle="Natural Language Processing and Information Systems",<br>year="2023",<br>publisher="Springer Nature Switzerland",<br>address="Cham",<br>pages="170--185",<br>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.",<br>isbn="978-3-031-35320-8"<br>}</p> <h2>Contact</h2> <p>If you have any questions or feedback, feel free to contact us at ria.hari.gusmita@uni-paderborn.de or ria.gusmita@uinjkt.ac.id</p>
Quran 3D Free
Salam Aleikum. You can use my Quran model your projects with free. Source: Objaverse 1.0 / Sketchfab
Sand Drawing, Verses Of Quran
Sand Drawing, Verses Of Quran Hand painting made from Sahara dune sand. The sand can contain more than one color which let artist develop some very interresting artwork Source: Objaverse 1.0 / Sketchfab
Quranic Perspective on Sustainability
<p>During the Sabai Webinar Series 4, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), Burmese Young Experts, including Mr Nay Oo Lwin, Associate ASEAN Engineer, Mr Tin Shine Aung, Sustainability Specialist, and Mr Kyaw Min Htike, Advocacy Director, engaged in a discussion on "Quranic Perspective on Sustainability".</p><p>They highlighted how the Quran provides foundational principles and guidance on sustainability, emphasising the interconnectedness of humanity and the environment.</p><p>This Sabai Webinar Series was conducted under the Edu4SD project.</p>
The Effect of Listening to the Holy Quran on Vital Signs in Coma Patients
ClinicalTrials.gov study NCT06326541. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Quranic Recitation and Classical Music on Pain Intensity and Interleukin-6 Levels After Lower Limb Orthopedic Surgery with Intrathecal Anesthesia
ClinicalTrials.gov study NCT06713044. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of the Quranic WAY Metaphor in Tafsir: A Corpus Analysis Approach
<p>This dataset contains the necessary data and scripts to recreate all steps and operations described in "Development of the Quranic WAY Metaphor in Tafsir: A Corpus Analysis Approach" by Adrian Bernhard. It also provides Docker configuration files and a database dump, allowing for quick recreation of the research infrastructure on which the study is based. Simply adapt the Docker configuration to include the database dump during setup. </p>
"AL-İTQAN Fİ ULUM AL-QUR'AN" İS A METHOD OF COVERİNG THE QURANİC SCİENCES
Open the record for dataset details and reuse information.
The Effect of Quran of Post Operative Pain
ClinicalTrials.gov study NCT02589834. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of Stress and Anxiety for Patients Undergoing MRI Using, Holy Quran, Music, and Natural Sounds
ClinicalTrials.gov study NCT06348862. IPD Sharing: YES. Countries: 1. Publications: 0.
The Effect of Listening to Holy Quran Recital on Depression, Anxiety and Stress Post CABG
ClinicalTrials.gov study NCT05419544. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effect of the Quran on Quality of Life and Quality of Sleep in Breast Cancer
ClinicalTrials.gov study NCT06893770. IPD Sharing: NO. Countries: 1. Publications: 0.
Knowledge of Students About Vitamin A and Assessment of Eye Manifestations in Quranic Schools (Khalwai) Khartoum State, Sudan 2022
ClinicalTrials.gov study NCT06989879. IPD Sharing: YES. Countries: 1. Publications: 0.
Additional Effects of Quranic Listening Meditation in Combination With High Intensity Interval Training in Obese Adults
ClinicalTrials.gov study NCT06442371. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Listening to Holy Quran Recital on the Incidence of Delirium Post-CABG
ClinicalTrials.gov study NCT06346990. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Effectiveness of Slow Back Stroke Massage and Murrotal Quran on Fatigue and Quality of Life of Patients Undergoing Haemodialysis
ClinicalTrials.gov study NCT05183815. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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