Latin writing styles analysis with Machine Learning: New approach to old questions
Not on the map. Why?
Abstract (arXiv)
In the Middle Ages texts were learned by heart and spread using oral means of communication from generation to generation. Adaptation of the art of prose and poems allowed keeping particular descriptions and compositions characteristic for many literary genres. Taking into account such a specific construction of literature composed in Latin, we can search for and indicate the probability patterns of familiar sources of specific narrative texts. Consideration of Natural Language Processing tools allowed us the transformation of textual objects into numerical ones and then application of machine learning algorithms to extract information from the dataset. We carried out the task consisting of the practical use of those concepts and observation to create a tool for analyzing narrative texts basing on open-source databases. The tool focused on creating specific search tools resources which could enable us detailed searching throughout the text. The main objectives of the study take into account finding similarities between sentences and between documents. Next, we applied machine learning algorithms on chosen texts to calculate specific features of them (for instance authorship or centuries) and to recognize sources of anonymous texts with a certain percentage.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 1 Sep 2021.
Builds on
6 of 21 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2014Distributed Representations of Sentences and Documentscited by 677
- 2020Latin BERT: A Contextual Language Model for Classical Philologycited by 43
- 2016Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learningcited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.