In the recent years, word embedding models are being proposed to effectively detect language change and semantic shift in diachronic corpora. In this paper, we present a comparative analysis of different word embedding approaches by considering a case-study based on an Italian diachronic corpus of Vatican publications of Popes from Leo XIII to Francis (1898-2020). Four different approaches are considered, characterized by the adoption of different embedding models each one trained over the publications of a specific pope. The paper aims to explore whether and how word embedding techniques are successful in detecting semantic shifts over the language used by popes .

Semantic Shift Detection in Vatican Publications: a Case Study from Leo XIII to Francis / S. Castano, A. Ferrara, S. Montanelli, F. Periti (CEUR WORKSHOP PROCEEDINGS). - In: SEBD 2022 : Italian Symposium on Advanced Database Systems / [a cura di] G. Amato, V. Bartalesi, D. Bianchini, C. Gennaro, R. Torlone. - [s.l] : CEUR-WS, 2022 Aug 22. - pp. 231-243 (( Intervento presentato al 30. convegno Italian Symposium on Advanced Database Systems tenutosi a Tirrenia nel 2022.

Semantic Shift Detection in Vatican Publications: a Case Study from Leo XIII to Francis

S. Castano
Primo
;
A. Ferrara
Secondo
;
S. Montanelli
Penultimo
;
F. Periti
Ultimo
2022

Abstract

In the recent years, word embedding models are being proposed to effectively detect language change and semantic shift in diachronic corpora. In this paper, we present a comparative analysis of different word embedding approaches by considering a case-study based on an Italian diachronic corpus of Vatican publications of Popes from Leo XIII to Francis (1898-2020). Four different approaches are considered, characterized by the adoption of different embedding models each one trained over the publications of a specific pope. The paper aims to explore whether and how word embedding techniques are successful in detecting semantic shifts over the language used by popes .
Computational Humanities; Word Embeddings; Semantic Shift Detection
Settore INF/01 - Informatica
https://ceur-ws.org/Vol-3194/paper29.pdf
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
paper29.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 1.49 MB
Formato Adobe PDF
1.49 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/946481
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact