@inproceedings{picca-pavlopoulos-2024-deciphering-emotional,
title = "Deciphering Emotional Landscapes in the {I}liad: A Novel {F}rench-Annotated Dataset for Emotion Recognition",
author = "Picca, Davide and
Pavlopoulos, John",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.399",
pages = "4462--4467",
abstract = "One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity{'}s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.",
}
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<abstract>One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity’s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.</abstract>
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%0 Conference Proceedings
%T Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition
%A Picca, Davide
%A Pavlopoulos, John
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F picca-pavlopoulos-2024-deciphering-emotional
%X One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity’s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.
%U https://aclanthology.org/2024.lrec-main.399
%P 4462-4467
Markdown (Informal)
[Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition](https://aclanthology.org/2024.lrec-main.399) (Picca & Pavlopoulos, LREC-COLING 2024)
ACL