An identification of authentic narrator's name features in Malay hadith texts

Rahman, Nurazzah Abd; Alias, Nursyahidah; Ismail, Normaly Kamal; Nor, Zulhilmi Mohamed; Alias, Muhammad Nazir

doi:10.1109/icos.2015.7377282

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…In [30], the researchers introduced a rule-based method for recognizing the names of narrators in Malay text. They handled two problems; first, the absence of standard transliteration between Arabic and Malay languages for narrators' names; second, the different forms of the same narrator's name in Arabic.…”

Section: B Narration-chains Processing Researchesmentioning

confidence: 99%

A Novel Hadith Processing Approach Based on Genetic Algorithms

Najeeb

2020

IEEE Access

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Quran and Hadith are the main religious sources in Islam. Hadith is mainly the saying of Prophet Mohammad, and it consists of two parts: first is ''Isnad''; which is the series of persons (narrators) who report (narrate) Hadith, and the second is ''Matn''; which is the saying or the narration itself. Isnad can be divided into phrases called ''Isnad-Phrases'', which could be tagged according to proposed Part-Of-Isnads (POIs) that represent the entity types of Isnad, such as Narrator Name, Prophet Name and Received Method. One of the main objectives of Hadith sciences is to ascertain the validity of Hadith and determine if it is accepted or rejected; this is known as Hadith judgment. Hadith scholars put many rules to judge Hadiths; some of these rules are related to the chain of narrators. Therefore, to judge Hadith, first, the narrators' names in Isnad should be extracted then judgment rules can be applied. Many researches proposed different methods to extract narrators' names from Isnad. To the best of the author's knowledge, there is no research process Isnad using Genetic Algorithms (GA). In this research, a novel approach is introduced for Isnad processing based on GA. This approach aims to predict the narrators' names and the other POIs for Isnad. The experiments, which conducted on all Hadiths narrated in ''Sahih Muslim'' book, show that the proposed approach achieved 81.44% accuracy. For further research in this genre, investigation of utilizing other approaches for Isnad processing, such as deep learning, is recommended. INDEX TERMS Arabic natural language processing, genetic algorithms, hadith, isnad, tagging.

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Section: B Narration-chains Processing Researchesmentioning

confidence: 99%

A Novel Hadith Processing Approach Based on Genetic Algorithms

Najeeb

2020

IEEE Access

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“…In their experiments, they used an English translation of the hadith books, performed their experiments on English data and used the conventional architectural framework for the NER as used by other researchers. Rahman et al (2015) presented an NER system for hadith text translated in Malay. Their work was based on the extraction of authentic narrator’s names from the hadith text.…”

Section: Related Workmentioning

confidence: 99%

KEFST: a knowledge extraction framework using finite-state transducers

Mahmood

Khan

Rehman

et al. 2019

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Purpose The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains. Design/methodology/approach This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results. Findings The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature. Originality/value This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

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“…There are many issues in Hadith studies as it has been summarized into 4 levels of Hadith studies in Ibrahim et al [3,4] as depicted in Figure 2. The subjects are changeable from the digitalization of the Hadith data [5][6][7][8][9][10][11][12][13][14][15] Hadith classification is an innovative research studies in computing fields that use different Data mining methods with a list of various options for the approach and algorithms such as decision tree, support vector machine (SVM), K-nearest neighbor (KNN), and Naive Bayes probabilistic classifier [23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Thematic Textual Hadith Classification: An Experiment in Rapidminer using Support Vector Machine (SVM) and Naïve Bayes Algorithm

Yusof¹

2020

IJATCSE

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There are many existing problems in Hadith studies trending in the study field. The issues are changeable from the digitalization of the Hadith data to an exact case study of estimation of narrators' chain for a particular Hadith. However, in this paper, we are not concentrating on the such learning of estimating, confirming or authenticating a Hadith. It focuses more on the data mining use to the Hadith dataset. We put on the Hadith dataset onto one of machine learning tools which is text classification. The Hadith dataset is put into experiment for Hadith textual classification. It concentrates on the thematic classification based on the themes and words occurrences from the Hadith text (matn). The Hadith textual classification does not trace on the hukm and position or class of Hadith. This research does not categorize the Hadith into hukm Sahih, Hasan, Dhaif, or Mawdhoo'. However, the Hadith thematic dataset of this study use only Hadith from Sahih Bukhari, where all Hadith in the Book is categorized as sahih by Imam Al-Bukhari. The classification for this thematic Hadith dataset is implemented using Rapidminer, a machine learning tool using Naïve Bayes and Support Vector Machine (SVM) methods. From the results, the different value of accuracy for both SVM and Naïve Bayes Algorithm was 2.4%. The Naïve Bayes Algorithm displayed better result comparing to SVM. We believe that the result could be better by improving the data, algorithms, algorithm tuning or ensemble methods for the future experiments.

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An identification of authentic narrator's name features in Malay hadith texts

Cited by 10 publications

References 6 publications

A Novel Hadith Processing Approach Based on Genetic Algorithms

A Novel Hadith Processing Approach Based on Genetic Algorithms

KEFST: a knowledge extraction framework using finite-state transducers

Thematic Textual Hadith Classification: An Experiment in Rapidminer using Support Vector Machine (SVM) and Naïve Bayes Algorithm

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