A hybrid method of rule-based approach and statistical measures for recognizing narrators name in hadith

Balgasem, Soad Saleh; Zakaria, Lailatul Qadri

doi:10.1109/iceei.2017.8312417

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…ey evaluated their method using the ANERcorp dataset and reported a 94% F-measure for the person name entity. Balgasem and Zakaria [20] proposed a hybrid approach to recognize Arabic names from Hadith.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

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Named entity recognition (NER) is fundamental in several natural language processing applications. It involves finding and categorizing text into predefined categories such as a person's name, location, and so on. One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents. The proposed method relied on triggers words, patterns, gazetteers, rules, and blacklists generated by the linguistic information about entities named in Arabic. The method operates in three stages, operational stage, preprocessing stage, and processing the rule application stage. The proposed approach was evaluated, and the results indicate that this approach achieved a 90.2% rate of precision, an 89.3% level of recall, and an F-measure of 89.5%. This new approach was introduced to overcome the challenges related to coverage in rule-based NER systems, especially when dealing with Classical Arabic texts. It improved their performance and allowed for automated rule updates. The grammar rules, gazetteers, blacklist, patterns, and trigger words were all integrated into the rule-based system in this way.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

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“…Evaluating the created model on various corpora demonstrates that it can achieve an accuracy of 80% and a recall of 73%. The author [8] constructed NERs for Arabic Hadith texts using three distinct approaches: rule-based, statistical, and hybrid (rule-based combined with statistical). The statistical methods used are the Log-likelihood Ratio (LLR), Point-wise Mutual Information (PMI), S-cost, R-cost, and U-cost.…”

Section: Related Workmentioning

confidence: 99%

BERT based Named Entity Recognition for Automated Hadith Narrator Identification

Luthfi¹,

Yusoh²,

Aboobaider³

2022

IJACSA

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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.

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“…Faidi et al [24] combined numerous classifiers and stemmers to compare Hadith classification tools, the experiments were conducted on 795 Hadiths from Sahih Al-Bukhari, and the best combination was the stemmer of Khoja with SVM, which got 57.50% as accuracy. Balgasem and Zakaria [25] used the log-likelihood ratio (LLR) with the rule-based approach to identifying narrators' names, the experiments were conducted on 235 Hadiths from Sahih Al-Bukhari, and the F-measure was 82%.…”

Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Najeeb

2022

Mathematical Problems in Engineering

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Hadith judgment implies checking the validity of Hadith to decide whether it is correct (trustworthy) or false (bogus). “Matn” and “Isnad” are the main constituents of Hadith; “Matn” is the sayings of the prophet, whereas “Isnad” represents the narrators’ series. The first step of Hadith judgment is the extraction of narrators’ names, after that, the rules of judgment, which were set out by Hadith’s scientists, could be implemented, three of these rules are particularly related to the narrators’ series, and these rules are continuity of the transmission chain, the trustworthiness of the narrators, and the preciseness of the narrators. Therefore, to check the authenticity of Hadiths, the three conditions must be satisfied, and to do so, the narrators’ names must be extracted first. Isnad contains many words and phrases called “Isnad-Phrases”; these phrases have many types or categories called part of Isnads (POIs) like Narrator-Name, Prophet-Name, and Received-Method. A lot of computational research studies suggest serving Hadith sciences by extracting the narrators’ names and other POIs using various approaches. This study presents a new hybrid approach founded on the hidden Markov model (HMM) and gazetteer lists to process “Isnad.” The approach objective is to expect all POIs in the Isnad including narrators’ names. The experiments carried on 1,000 Hadiths from “Sahih Muslim”: 900 Hadiths as training dataset and 100 Hadiths as testing dataset, and the results show a noteworthy accuracy for the proposed hybrid approach.

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A hybrid method of rule-based approach and statistical measures for recognizing narrators name in hadith

Cited by 9 publications

References 6 publications

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

BERT based Named Entity Recognition for Automated Hadith Narrator Identification

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

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