Abstract
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.
Highlights
Hadith is the second enactment root in Islam behind Quran; it represents the entire life of Prophet Muhammad, such as his deeds, sayings, and actions. e main constituents of Hadith are as follows: “Isnad” and “Matn,” and Isnad represents the narrators’ series, whereas “Matn” is the saying of the prophet
The hidden Markov model (HMM) is one of the significant methods used in natural language processing (NLP); it predicts new observations depending on previous states [7]
Names Classification of Hadiths using k-nearest neighbor, Naıve Bayes (NB), and support vector machine (SVM) Construction of information retrieval (IR) system depending on the conditional random field (CRF) and finite-state transducers (FSTs)
Summary
Hadith is the second enactment root in Islam behind Quran; it represents the entire life of Prophet Muhammad, such as his deeds, sayings, and actions. e main constituents of Hadith are as follows: “Isnad” and “Matn,” and Isnad represents the narrators’ series, whereas “Matn” is the saying of the prophet. The hidden Markov model (HMM) is one of the significant methods used in natural language processing (NLP); it predicts new observations depending on previous states [7]. Is study presents a new hybrid approach based on the hidden Markov model (HMM) and gazetteer lists to process “Isnad.” e rest of this study is organized as follows: Section 2 presents the. Emission probability matrix After each transition is made, a symbol is an output based on the emission probability matrix, which depends on the current state
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