Fenomeni di intertestualità tematica e di situazione nella letteratura classica
Research
Fenomeni di intertestualità tematica e di situazione nella letteratura classica
Responsabili scientifici: Marco Fucecchi, Maddalena Zunino, Andrea Brunello
The rich fabric of Greek and Latin literature offers a fertile ground for the exploration of intertextuality, where texts echo, reference, or directly cite earlier works, creating a dense network of linguistic and thematic connections. This project aims to investigate the complex domain of intertextuality within classical Greek and Latin texts, employing computational techniques such as Natural Language Processing (NLP), textual analysis, and machine learning.
Objectives
The goal is to uncover the intricate web of thematic and semantic correlations, references, and echoes between different texts, thereby providing a deeper understanding of the dialogues between authors and distinct works.
Results in the field of DH
Through topic extraction, we aim to identify recurring themes and subjects that permeate different texts, offering insights into the predominant cultural and social discourses of the time. Additionally, by leveraging named entity recognition, we seek to map the multitude of characters, places, and events that intertwine within Greek and Latin literature. More complex linguistic aspects, such as metaphors and idiomatic expressions, can be approached using deep learning and embeddings, allowing the model to recognize semantic similarities beyond mere syntactic analogies. This exploration not only seeks to illuminate the nuanced dialogues occurring within Latin and Greek literature but also to push the boundaries of what can be achieved with computational linguistics and artificial intelligence in classical studies.
