<span>Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of <em>Mamdani</em>-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.</span>
Text document expresses enormous sort of information traditional database. Unstructured data, particularly free running text data has to be transformed into a structured data. Extracting information from text is part of NLP process.The implementation of the NER algorithm for NLP is normally influenced by the domain of the studies. Besides, there is no existing system that is designed to detect the types of named entity in hadith text, develop POS tags and rule based extraction for narrator's name in Hadith Text in the Malay language. The POS tags were developed from 1000 hadith te tags were created involving a total of 256 words which is part of narrator's names. The rule based was developed to determine five types of narrator's chain. Further research will determine the relationship between each narrator and the constr Text document expresses enormous sort of information but it lacks the imposed structure of a nstructured data, particularly free running text data has to be transformed into a structured data. Extracting information from text is part of NLP process.The implementation of the NER algorithm for NLP is normally influenced by the domain of , there is no existing system that is designed to detect the types of named entity in hadith text, develop POS tags and rule based extraction for narrator's name in HadithText in the Malay language. The POS tags were developed from 1000 hadith te tags were created involving a total of 256 words which is part of narrator's names. The rule based was developed to determine five types of narrator's chain. Further research will determine the relationship between each narrator and the construction of narration's chain.tagging; hadith text; name.: syahidah@uitm.edu.my
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