Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language

Bernardo Magnini, Alberto Lavelli, Simone Magnolini


Abstract
We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared tasks) on two sequence tagging tasks (i.e., named entities recognition and nominal entities recognition) and four classification tasks (i.e., lexical relations among words, semantic relations among sentences, sentiment analysis and text classification). We show that deep learning approaches outperform traditional machine learning algorithms in sequence tagging, while for classification tasks that heavily rely on semantics approaches based on feature engineering are still competitive. We think that a similar analysis could be carried out for other languages to provide an assessment of machine learning / deep learning models across different languages.
Anthology ID:
2020.lrec-1.259
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2110–2119
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.259
DOI:
Bibkey:
Cite (ACL):
Bernardo Magnini, Alberto Lavelli, and Simone Magnolini. 2020. Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2110–2119, Marseille, France. European Language Resources Association.
Cite (Informal):
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language (Magnini et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.259.pdf