@inproceedings{dinu-etal-2024-itgrasyll,
title = "{I}t{G}ra{S}yll: A Computational Analysis of Graphical Syllabification and Stress Assignment in {I}talian",
author = "Dinu, Liviu and
Iordache, Ioan-Bogdan and
Georgescu, Simona and
Cristea, Alina Maria and
Guita, Bianca",
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-1.38/",
pages = "316--324",
ISBN = "979-12-210-7060-6",
abstract = "In this paper we build a dataset of Italian syllables. We perform quantitative and qualitative analyses on the syllabification and stress assignment in Italian. We propose a machine learning model, based on deep-learning techniques, for automatically inferring syllabification and stress assignment. For stress prediction we report 94.45{\%} word-level accuracy, and for syllabification we report 98.41{\%} word-level accuracy and 99.82{\%} hyphen-level accuracy."
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<abstract>In this paper we build a dataset of Italian syllables. We perform quantitative and qualitative analyses on the syllabification and stress assignment in Italian. We propose a machine learning model, based on deep-learning techniques, for automatically inferring syllabification and stress assignment. For stress prediction we report 94.45% word-level accuracy, and for syllabification we report 98.41% word-level accuracy and 99.82% hyphen-level accuracy.</abstract>
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%0 Conference Proceedings
%T ItGraSyll: A Computational Analysis of Graphical Syllabification and Stress Assignment in Italian
%A Dinu, Liviu
%A Iordache, Ioan-Bogdan
%A Georgescu, Simona
%A Cristea, Alina Maria
%A Guita, Bianca
%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 dinu-etal-2024-itgrasyll
%X In this paper we build a dataset of Italian syllables. We perform quantitative and qualitative analyses on the syllabification and stress assignment in Italian. We propose a machine learning model, based on deep-learning techniques, for automatically inferring syllabification and stress assignment. For stress prediction we report 94.45% word-level accuracy, and for syllabification we report 98.41% word-level accuracy and 99.82% hyphen-level accuracy.
%U https://aclanthology.org/2024.clicit-1.38/
%P 316-324
Markdown (Informal)
[ItGraSyll: A Computational Analysis of Graphical Syllabification and Stress Assignment in Italian](https://aclanthology.org/2024.clicit-1.38/) (Dinu et al., CLiC-it 2024)
ACL