Towards Analysis of Biblical Entities and Names using Deep Learning

Scholars from various fields have studied the translations of the Bible in different languages to understand the changes that have occurred over time. Taking into account recent advances in deep learning, there is an opportunity to improve the understanding of these texts and conduct analyses that were previously unattainable. This study used deep learning techniques of NLP to analyze the distribution and appearance of names in the Polish, Croatian, and English translations of the Gospel of Mark. Within the scope of social network analysis (SNA), various centrality metrics were used to determine the importance of different entities (names) within the gospel. Degree Centrality, Closeness Centrality, and Betweenness Centrality were leveraged, given their capacity to provide unique insights into the network structure. The findings of this study demonstrate that deep learning could help uncover interesting connections between individuals who may have initially been considered less important. It also highlighted the critical role of onomastic sciences and the philosophy of language in analyzing the richness and importance of human and other proper names in biblical texts. Further research should be conducted to produce more relevant language resources, improve parallel multilingual corpora and annotated data sets for the major languages of the Bible, and develop an accurate end-to-end deep neural model that facilitates joint entity recognition and resolution.