RESEARCH DESCRIPTION
The complex interconnections within Greek and Latin literature provides 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 Greek and Latin classical texts, employing computational techniques such as Natural Language Processing (NLP), text analysis, and machine learning. The goal is to unveil the intricate web of thematic and semantic correlations, references, and echoes among different texts, thus providing a deeper understanding of the dialogues between authors and works. Through topic extraction, we aim to identify recurring themes and subjects that permeate various texts, offering insights into the prevailing cultural and social discourses of the period. Furthermore, by leveraging named entity recognition, we seek to map the multitude of characters, places, and events interwoven in Greek and Latin literature. More complex linguistic features, such as metaphors and idiomatic expressions, can be addressed through deep learning and embeddings, allowing the model to recognize semantic similarities beyond simple syntactic analogies. This exploration not only seeks to illuminate the multifaceted dialogues within Latin and Greek literature, but also to push the boundaries of what is achievable with computational linguistics and artificial intelligence in the field of classical studies.
Technological Tools and Methods
- NLP libraries: for instance, NLTK, SpaCy and Gensim libraries for Python
- Text analysis techniques: ranging from classical ones, such as LDA/LSA, to the usage of modern deep learning embeddings (PyTorch library for Python, pre-trained models such as Latin BERT and Ancient Greek BERT)
- Data Visualization: Matplotlib and Seaborn libraries for Python, NetworkX
- Database Management: relational (Postgres) and non-relational solutions (graph databases: Neo4J, document-oriented stores: MongoDB)
OPPORTUNITIES FOR PARTICIPATION IN THE PROJECT
- Bachelor’s degree project: implement basic text pre-processing and analysis and NLP techniques to explore thematic parallels across selected Latin texts using classical tools, such as LDA and LSA
- Master’s degree project: dive deeper into specific techniques or aspects of intertextuality, possibly incorporating more advanced machine learning or deep learning models (e.g., Latin BERT embeddings)
- Internship: engage in a focused aspect of the project, such as data cleaning, basic analysis, or data management through a database, to gain hands-on experience with NLP and text analysis in a real-world project setting, contributing to a particular phase or aspect of the project
- PhD research project: engage in a thorough, in-depth exploration of intertextuality in Latin literature using and developing new advanced computational methods and database systems solutions, with the idea of creating a full-fledged framework to handle intertextuality in the classic Greek and Latin literature
