Tagging Algorithm and POS Tags for Narrator's Name in Hadith Document

Named Entity Recognition (NER) is important in many domains, such as information retrieval and text classification. Typically, NER uses machine learning (ML) or rule-based methods to recognize NER. ML works well with annotated corpora. The most commonly used annotated corpora are in English but not in Malay. Non-annotated corpora need to be tagged to be used in NER, and manual tagging requires time and effort from experts. In this work, we proposed a Natural Language Processing (NLP) technique to automate the tagging of narrators in hadith texts. Our proposed technique includes a model for tagging narrators in hadith texts. This research used POS tags to explain the detection of narrators' names in hadith texts. A total of 700 hadith texts were used to develop the tagging model using a rule-based technique. 300 hadith documents were examined to evaluate the tagging model and resulted in 93.64% for recall and 96.72% for precision. Further research is needed to investigate the automatic extraction of the narrators' names, which can be used in hadith retrieval.