Standard
DORE: A Dataset for Portuguese Definition Generation. / Furtado, Anna Beatriz Dimas
; Ranasinghe, Tharindu; Blain, Frederic et al.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ed. / Nicoletta Calzolari; Min-Yen Kan; Veronique Hoste; Alessandro Lenci; Sakriani Sakti; Nianwen Xue. ELRA and ICCL, 2024. p. 5315-5322.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Furtado, ABD
, Ranasinghe, T, Blain, F
& Mitkov, R 2024,
DORE: A Dataset for Portuguese Definition Generation. in N Calzolari, M-Y Kan, V Hoste, A Lenci, S Sakti & N Xue (eds),
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL, pp. 5315-5322, The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, Torino, Italy,
20/05/24. <
https://aclanthology.org/2024.lrec-main.473/>
APA
Furtado, A. B. D.
, Ranasinghe, T., Blain, F.
, & Mitkov, R. (2024).
DORE: A Dataset for Portuguese Definition Generation. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.),
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 5315-5322). ELRA and ICCL.
https://aclanthology.org/2024.lrec-main.473/Vancouver
Furtado ABD
, Ranasinghe T, Blain F
, Mitkov R.
DORE: A Dataset for Portuguese Definition Generation. In Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL. 2024. p. 5315-5322
Author
Furtado, Anna Beatriz Dimas
; Ranasinghe, Tharindu ; Blain, Frederic et al. /
DORE : A Dataset for Portuguese Definition Generation. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). editor / Nicoletta Calzolari ; Min-Yen Kan ; Veronique Hoste ; Alessandro Lenci ; Sakriani Sakti ; Nianwen Xue. ELRA and ICCL, 2024. pp. 5315-5322
Bibtex
@inproceedings{8d0a788ecabc4986b0f7f90acc81f64e,
title = "DORE: A Dataset for Portuguese Definition Generation",
abstract = "Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.",
author = "Furtado, {Anna Beatriz Dimas} and Tharindu Ranasinghe and Frederic Blain and Ruslan Mitkov",
year = "2024",
month = may,
day = "20",
language = "English",
isbn = "9782493814104",
pages = "5315--5322",
editor = "Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
publisher = "ELRA and ICCL",
note = " The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 ; Conference date: 20-05-2024 Through 25-05-2024",
url = "https://lrec-coling-2024.org/",
}
RIS
TY - GEN
T1 - DORE
T2 - The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation
AU - Furtado, Anna Beatriz Dimas
AU - Ranasinghe, Tharindu
AU - Blain, Frederic
AU - Mitkov, Ruslan
PY - 2024/5/20
Y1 - 2024/5/20
N2 - Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.
AB - Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.
M3 - Conference contribution/Paper
SN - 9782493814104
SP - 5315
EP - 5322
BT - Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
A2 - Calzolari, Nicoletta
A2 - Kan, Min-Yen
A2 - Hoste, Veronique
A2 - Lenci, Alessandro
A2 - Sakti, Sakriani
A2 - Xue, Nianwen
PB - ELRA and ICCL
Y2 - 20 May 2024 through 25 May 2024
ER -
