Embedded in the Labyrinth. Investigating Latin Word Senses through Transformer-Based Contextual Embeddings and Attention

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985955%3A_____%2F25%3A00642012" target="_blank" >RIV/67985955:_____/25:00642012 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Embedded in the Labyrinth. Investigating Latin Word Senses through Transformer-Based Contextual Embeddings and Attention

Popis výsledku v původním jazyce

This paper explores how transformer-based models can enhance historical keyword-in-context studies through automatic word sense disambiguation (WSD). Using the Latin term labyrinthus as a case study, we analyze its contextual meanings across time and genre within the GreLa corpus. A Large language model provides preliminary sense labels, which we use to evaluate 64 embedding variants—contextual, attention-based, and co-occurrence-based—derived from XLM-R and Latin BERT. Our results show that combining embedding types yields the best performance. We also illustrate how attention-based embeddings capture meaningful diachronic patterns, offering promising directions for future research on semantic change and metaphor in historical texts.

Název v anglickém jazyce

Embedded in the Labyrinth. Investigating Latin Word Senses through Transformer-Based Contextual Embeddings and Attention

Popis výsledku anglicky

This paper explores how transformer-based models can enhance historical keyword-in-context studies through automatic word sense disambiguation (WSD). Using the Latin term labyrinthus as a case study, we analyze its contextual meanings across time and genre within the GreLa corpus. A Large language model provides preliminary sense labels, which we use to evaluate 64 embedding variants—contextual, attention-based, and co-occurrence-based—derived from XLM-R and Latin BERT. Our results show that combining embedding types yields the best performance. We also illustrate how attention-based embeddings capture meaningful diachronic patterns, offering promising directions for future research on semantic change and metaphor in historical texts.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

60301 - Philosophy, History and Philosophy of science and technology

Projekt

<a href="/cs/project/LL2320" target="_blank" >LL2320: Počátky novověkého encyklopedismu: Spuštění evoluční metaforologie</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů