2022/10/14 by Vincenzo Perri, Lisi Qarkaxhija, Perri, Vincenzo +7
Arts and Humanities · Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Humanities and Scholarship #FOS: Computer and information sciences #Folklore, Mythology, and Literature Studies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2210.07871
openalex publication_date 2022/10/14 · openalex created_date 2022/10/19 · openalex updated_date 2026/07/28
Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies. Similarly, the construction of co-occurrence networks of literary characters, and their analysis using methods from social network analysis and network science, have provided insights into the micro- and macro-level structure of literary texts. Combining these perspectives, in this work we study character networks extracted from a text corpus of J.R.R. Tolkien's Legendarium. We show that this perspective helps us to analyse and visualise the narrative style that characterises Tolkien's works. Addressing character classification, embedding and co-occurrence prediction, we further investigate the advantages of state-of-the-art Graph Neural Networks over a popular word embedding method. Our results highlight the large potential of graph learning in Computational Literary Studies.