Classification; Corpus driven; Deep learning; Intertextuality; Case-studies; Hybrid architectures; Learning architectures; Multi channel; New approaches; Reuse; Textual data; Computer Science Applications; Information Systems; Information Systems and Management; Analysis
Abstract :
[en] The detection of intertextuality is at the heart of many linguistic studies. A lot of efforts are currently underway to provide tools for analyzing relationships between authors. Most of them use standard statistics to compare textual data and find traces of text reuse from one author to another. The main objective of this work is to provide a new approach based on deep learning architectures and corpus-driven analysis. Building on previous contributions, we propose a hybrid architecture called multichannel convolutional transformer (MCT). Using this method, we develop a new tool for intertextuality detection based on authorship attribution. We have empirically demonstrated its efficiency using a Latin corpus. We conclude that our model can highlight complex linguistic patterns as features responsible for the classification decision. We consider these patterns as new categories of intertextuality traces, complementary to the existing ones.
Disciplines :
Languages & linguistics
Author, co-author :
Vanni, Laurent; CNRS, UCA, Nice, France
Mahmoudi, Hadi; UCA, Nice, France
Longrée, Dominique ; Université de Liège - ULiège > Département des sciences de l'antiquité > Langue et littérature latines
Mayaffre, Damon; CNRS, Nice, France
Language :
English
Title :
Multichannel Convolutional Transformer and Intertextuality: A Latin Case Study
Publication date :
2024
Event name :
JADT
Event place :
Naples, Ita
Event date :
06-07-2022 => 08-07-2022
Audience :
International
Main work title :
New Frontiers in Textual Data Analysis
Editor :
Giordano, Giuseppe
Publisher :
Springer Science and Business Media Deutschland GmbH
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