@inproceedings{magnini-etal-2020-comparing,
title = "Comparing Machine Learning and Deep Learning Approaches on {NLP} Tasks for the {I}talian Language",
author = "Magnini, Bernardo and
Lavelli, Alberto and
Magnolini, Simone",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.259",
pages = "2110--2119",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language
%A Magnini, Bernardo
%A Lavelli, Alberto
%A Magnolini, Simone
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F magnini-etal-2020-comparing
%X 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.
%U https://aclanthology.org/2020.lrec-1.259
%P 2110-2119
Markdown (Informal)
[Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language](https://aclanthology.org/2020.lrec-1.259) (Magnini et al., LREC 2020)
ACL