@inproceedings{celli-basile-2024-history,
title = "History Repeats: Historical Phase Recognition from Short Texts",
author = "Celli, Fabio and
Basile, Valerio",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.24/",
pages = "198--204",
ISBN = "979-12-210-7060-6",
abstract = "This paper introduces a new multi-class classification task: the prediction of the Structural-Demographic phase of historical cycles - such as growth, impoverishment and crisis - from text describing historical events. To achieve this, we leveraged data from the Seshat project, annotated it following specific guidelines and then evaluated the consistency between three annotators. The classification experiments, with transformers and Large Language Models, show that 2 of 5 phases can be detected with good accuracy. We believe that this task could have a great impact on comparative history and can be helped by event extraction in NLP."
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%0 Conference Proceedings
%T History Repeats: Historical Phase Recognition from Short Texts
%A Celli, Fabio
%A Basile, Valerio
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F celli-basile-2024-history
%X This paper introduces a new multi-class classification task: the prediction of the Structural-Demographic phase of historical cycles - such as growth, impoverishment and crisis - from text describing historical events. To achieve this, we leveraged data from the Seshat project, annotated it following specific guidelines and then evaluated the consistency between three annotators. The classification experiments, with transformers and Large Language Models, show that 2 of 5 phases can be detected with good accuracy. We believe that this task could have a great impact on comparative history and can be helped by event extraction in NLP.
%U https://aclanthology.org/2024.clicit-1.24/
%P 198-204
Markdown (Informal)
[History Repeats: Historical Phase Recognition from Short Texts](https://aclanthology.org/2024.clicit-1.24/) (Celli & Basile, CLiC-it 2024)
ACL