Beschreibung:
This paper explores new methods for disambiguating the identity of individuals in classical Arabic citations (isnāds) using a network-based approach. After training a model to extract name mentions from classical Arabic, we embed these mentions in vector space using fine-tuned BERT representations and use community detection to infer clusters of coreferent mentions. The best-performing clustering approach reduces error on the CoNLL metric by 30%. Then, as a case study, we examine the problem of determining the number of direct transmitters to Ibn ʿAsākir (d. 1176) in a set of isnāds taken from the 12th century historical text Taʾrīkh Madīnat Dimashq (TMD, History of Damascus), using our method to replicate human judgement.