Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian

Ranka Stankovic, Branislava Šandrih, Cvetana Krstev, Miloš Utvić, Mihailo Skoric


Abstract
The training of new tagger models for Serbian is primarily motivated by the enhancement of the existing tagset with the grammatical category of a gender. The harmonization of resources that were manually annotated within different projects over a long period of time was an important task, enabled by the development of tools that support partial automation. The supporting tools take into account different taggers and tagsets. This paper focuses on TreeTagger and spaCy taggers, and the annotation schema alignment between Serbian morphological dictionaries, MULTEXT-East and Universal Part-of-Speech tagset. The trained models will be used to publish the new version of the Corpus of Contemporary Serbian as well as the Serbian literary corpus. The performance of developed taggers were compared and the impact of training set size was investigated, which resulted in around 98% PoS-tagging precision per token for both new models. The sr_basic annotated dataset will also be published.
Anthology ID:
2020.lrec-1.487
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:
3954–3962
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.487
DOI:
Bibkey:
Cite (ACL):
Ranka Stankovic, Branislava Šandrih, Cvetana Krstev, Miloš Utvić, and Mihailo Skoric. 2020. Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 3954–3962, Marseille, France. European Language Resources Association.
Cite (Informal):
Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian (Stankovic et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.487.pdf