Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing

Rob van der Goot, Alan Ramponi, Tommaso Caselli, Michele Cafagna, Lorenzo De Mattei


Abstract
Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen’s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normit.
Anthology ID:
2020.lrec-1.769
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6272–6278
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.769
DOI:
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PDF:
https://aclanthology.org/2020.lrec-1.769.pdf