Query based information retrieval and knowledge extraction using Hadith datasets
Ahsan Mahmood, Hikmat Ullah Khan, Zahoor-ur-Rehman, Wahab A. Khan · 2017
In Natural language processing, one of the fundamental tasks is Named Entity Recognition (NER) that include identifying names of peoples, locations and other entities. Applications of NER include catboats, speech recognition, machine translation, knowledge extraction and intelligent search systems. NER is an active research domain for the last 10 years. In this paper, we propose a knowledge extraction framework to extract Named entities from Sahih AlBukhari Urdu translation which is a world known Hadith book. The proposed framework is based on finite state transducer system to extract entities and process the Hadith content using Part of Speech (POS) tagging. Conditional Random Field, an ensemble based algorithm, processes the extracted nouns for NER and classification. In the future, we aim to implement the proposed framework to rank the hadith content and apply the Vector Space Model.