Large Language Models for Latvian Named Entity Recognition

• Rinalds Vīksna ^[1] ; Inguna Skadiņa ^[1]
  1. [1] University of Latvia

     University of Latvia

     Letonia

     
• Localización: Human Language Technologies – The Baltic Perspective: Proceedings of the Ninth International Conference Baltic HLT 2020 / coord. por Andrius Utka, Jurgita Vaičenonienė, Jolanta Kovalevskaitė, Danguolė Kalinauskaitė, 2024, ISBN 978-1-64368-116-0, págs. 62-69
• Idioma: inglés
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• Resumen

  • Transformer-based language models pre-trained on large corpora have demonstrated good results on multiple natural language processing tasks for widely used languages including named entity recognition (NER). In this paper, we investigate the role of the BERT models in the NER task for Latvian. We introduce the BERT model pre-trained on the Latvian language data. We demonstrate that the Latvian BERT model, pre-trained on large Latvian corpora, achieves better results (81.91 F1-measure on average vs 78.37 on M-BERT for a dataset with nine named entity types, and 79.72 vs 78.83 on another dataset with seven types) than multilingual BERT and outperforms previously developed Latvian NER systems.