Exploring the Use of Foundation Models for Named Entity Recognition and Lemmatization Tasks in Slavic Languages
This work addresses the problem of natural language processing for Slavic languages, but it is incremental as it applies existing methods to a specific domain.
The paper tackled named entity recognition and lemmatization in Slavic languages using foundation models like BERT and T5, achieving high metrics scores in both tasks.
This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER. The task involves the identification, categorization, and lemmatization of named entities in Slavic languages. Our approach involved exploring the use of foundation models for these tasks. In particular, we used models based on the popular BERT and T5 model architectures. Additionally, we used external datasets to further improve the quality of our models. Our solution obtained promising results, achieving high metrics scores in both tasks. We describe our approach and the results of our experiments in detail, showing that the method is effective for NER and lemmatization in Slavic languages. Additionally, our models for lemmatization will be available at: https://huggingface.co/amu-cai.