Publication details
Introducing a Gold Standard Corpus from Young Multilinguals for the Evaluation of Automatic UD-PoS Taggers for Italian
| Authors | |
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| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | 8th Italian Conference on Computational Linguistics, CLiC-it 2021 |
| MU Faculty or unit | |
| Citation | |
| Web | Full paper |
| Keywords | PoS tagging; automatic evaluation |
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| Description | Part-of-speech (PoS) tagging constitutes a common task in Natural Language Processing (NLP), given its widespread applicability. However, with the advance of new information technologies and language variation, the contents and methods for PoS-tagging have changed. The majority of Italian existing data for this task originate from standard texts, where language use is far from multifaceted informal real-life situations. Automatic PoS-tagging models trained with such data do not perform reliably on non-standard language, like social media content or language learners’ texts. Our aim is to provide additional training and evaluation data from language learners tagged in Universal Dependencies (UD), as well as testing current automatic PoStagging systems and evaluating their performance on such data. We use a multilingual corpus of young language learners, LEONIDE, to create a tagged gold standard for evaluating UD PoStagging performance on the Italian nonstandard language. With the 3.7 version of Stanza, a Python NLP package, we apply available automatic PoS-taggers, namely ISDT, ParTUT, POSTWITA, TWITTIRO and VIT, trained with both standard and non-standard data, on our dataset. Our results show that the above taggers, trained on non-standard data or multilingual Treebanks, can achieve up to 95% of accuracy on multilingual learner data, if combined. |