Linguistic features evaluation for hadith authenticity through automatic machine learning.
There has not been any research that provides an evaluation of the linguistic features extracted from the matn (text) of a Hadith. Moreover, none of the fairly large corpora are publicly available as a benchmark corpus for Hadith authenticity, and there is a need to build a 'gold standard' corpus fo...
| Published in: | Digital Scholarship in the Humanities Vol. 37; no. 3; pp. 830 - 844 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Sep2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=158667350&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 158667350 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2022 vid: 37 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 158667350 10.1093/llc/fqab092 ppf: 830 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.5MB tig: atl: Linguistic features evaluation for hadith authenticity through automatic machine learning. aug: au: Mohamed, Emad Sarwar, Raheem affil: Research Group in Computational Linguistics, University of Wolverhampton , UK su: Naive Bayes classification Machine learning Hadith Python programming language Digital humanities sug: subj: Naive Bayes classification Machine learning Hadith Python programming language Digital humanities ab: There has not been any research that provides an evaluation of the linguistic features extracted from the matn (text) of a Hadith. Moreover, none of the fairly large corpora are publicly available as a benchmark corpus for Hadith authenticity, and there is a need to build a 'gold standard' corpus for good practices in Hadith authentication. We write a scraper in Python programming language and collect a corpus of 3,651 authentic prophetic traditions and 3,593 fake ones. We process the corpora with morphological segmentation and perform extensive experimental studies using a variety of machine learning algorithms, mainly through automatic machine learning, to distinguish between these two categories. With a feature set including words, morphological segments, characters, top N words, top N segments, function words, and several vocabulary richness features, we analyze the results in terms of both prediction and interpretability to explain which features are more characteristic of each class. Many experiments have produced good results and the highest accuracy (i.e. 78.28%) is achieved using word n-grams as features using the Multinomial Naive Bayes classifier. Our extensive experimental studies conclude that, at least for Digital Humanities, feature engineering may still be desirable due to the high interpretability of the features. The corpus and software (scripts) will be made publicly available to other researchers in an effort to promote progress and replicability. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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