Learning to Identify Narrators in Classical Arabic Texts

One widespread historical method of transmitting and recording information about important events and people in the Middle East is the narration-based method. In this method, each saying about a person or event is transmitted from person to person until a systematic collector records and compiles such sayings in a stable collection. At each stage of transmission, the narrator not only transmits the saying but also the person he got it from going back to the earliest narrator. Identifying each narrator in these collections is important to better measure the accuracy of the narrations and identify the date and geographies of their circulation. In this work, we propose a natural language processing technique to automate the identification of narrators in classical Arabic texts. Our proposed technique consists of two models: 1) a model for detecting the narrators in the text, and 2) a model for linking narrators to their biographies. We train our two models on a large collection of annotated classical Arabic texts and achieve F1-scores of 96.15% and 95.74% for narration detection and linking respectively.