@inproceedings{laurs-2024-towards,
    title = "Towards a Readability Formula for {L}atin",
    author = "Laurs, Thomas",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.19",
    pages = "170--175",
    abstract = "This research focuses on the development of a readability formula for Latin texts, a much-needed tool to assess the difficulty of Latin texts in educational settings. This study takes a comprehensive approach, exploring more than 100 linguistic variables, including lexical, morphological, syntactical, and discourse-related factors, to capture the multifaceted nature of text difficulty. The study incorporates a corpus of Latin texts that were assessed for difficulty, and their evaluations were used to establish the basis for the model. The research utilizes natural language processing tools to derive linguistic predictors, resulting in a multiple linear regression model that explains about 70{\%} of the variance in text difficulty. While the model{'}s precision can be enhanced by adding further variables and a larger corpus, it already provides valuable insights into the readability of Latin texts and offers the opportunity to examine how different text genres and contents influence text accessibility. Additionally, the formula{'}s focus on objective text difficulty paves the way for future research on personal predictors, particularly in educational contexts.",
}

This research focuses on the development of a readability formula for Latin texts, a much-needed tool to assess the difficulty of Latin texts in educational settings. This study takes a comprehensive approach, exploring more than 100 linguistic variables, including lexical, morphological, syntactical, and discourse-related factors, to capture the multifaceted nature of text difficulty. The study incorporates a corpus of Latin texts that were assessed for difficulty, and their evaluations were used to establish the basis for the model. The research utilizes natural language processing tools to derive linguistic predictors, resulting in a multiple linear regression model that explains about 70{\%} of the variance in text difficulty. While the model{'}s precision can be enhanced by adding further variables and a larger corpus, it already provides valuable insights into the readability of Latin texts and offers the opportunity to examine how different text genres and contents influence text accessibility. Additionally, the formula{'}s focus on objective text difficulty paves the way for future research on personal predictors, particularly in educational contexts.
Laurs, Thomas
Towards a Readability Formula for Latin