Bianca Guita

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2024

pdf bib
ItGraSyll: A Computational Analysis of Graphical Syllabification and Stress Assignment in Italian
Liviu Dinu | Ioan-Bogdan Iordache | Simona Georgescu | Alina Maria Cristea | Bianca Guita
Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024)

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.
Search
Co-authors
Venues
Fix data