Abstract
Named entity recognition, in particular for morphological rich languages, is challenging task due to the richness of inflected forms and ambiguity. This challenge is being addressed by SlavNER Shared Task. In this paper we describe system submitted to this task. Our system uses pre-trained multilingual BERT Language Model and is fine-tuned for six Slavic languages of this task on texts distributed by organizers. In our experiments this multilingual NER model achieved 96 F1 score on in-domain data and an F1 score of 83 on out of domain data. Entity coreference module achieved F1 score of 47.6 as evaluated by bsnlp2021 organizers.- Anthology ID:
- 2021.bsnlp-1.11
- Volume:
- Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing
- Month:
- April
- Year:
- 2021
- Address:
- Kiyv, Ukraine
- Venue:
- BSNLP
- SIG:
- SIGSLAV
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 93–97
- Language:
- URL:
- https://aclanthology.org/2021.bsnlp-1.11
- DOI:
- Cite (ACL):
- Rinalds Vīksna and Inguna Skadina. 2021. Multilingual Slavic Named Entity Recognition. In Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing, pages 93–97, Kiyv, Ukraine. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual Slavic Named Entity Recognition (Vīksna & Skadina, BSNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.bsnlp-1.11.pdf