Identifying intertextual relationships between authors is of central importance to the study of literature. We report an empirical analysis of intertextuality in classical Latin literature using word embedding models. To enable quantitative evaluation of intertextual search methods, we curate a new dataset of 945 known parallels drawn from traditional scholarship on Latin epic poetry. We train an optimized word2vec model on a large corpus of lemmatized Latin, which achieves state-of-the-art performance for synonym detection and outperforms a widely used lexical method for intertextual search. We then demonstrate that training embeddings on very small corpora can capture salient aspects of literary style and apply this approach to replicate a previous intertextual study of the Roman historian Livy, which relied on hand-crafted stylometric features. Our results advance the development of core computational resources for a major premodern language and highlight a productive avenue for cross-disciplinary collaboration between the study of literature and NLP.

This paper announces version 1.0 of the Classical Language Toolkit (CLTK), an NLP framework for pre-modern languages. The vast majority of NLP, its algorithms and software, is created with assumptions particular to living languages, thus neglecting certain important characteristics of largely non-spoken historical languages. Further, scholars of pre-modern languages often have different goals than those of living-language researchers. To fill this void, the CLTK adapts ideas from several leading NLP frameworks to create a novel software architecture that satisfies the unique needs of pre-modern languages and their researchers. Its centerpiece is a modular processing pipeline that balances the competing demands of algorithmic diversity with pre-configured defaults. The CLTK currently provides pipelines, including models, for almost 20 languages.