Enhancing Named Entity Recognition in Portuguese Literary Texts with Adaptive Models (Linguamática, 2025)
We investigate pre-training strategies to enhance Named Entity Recognition (NER) in Portuguese literary texts. We introduce two domain-adaptive models, LitBERT-CRF and LitBERTimbau, built on general-domain language models. We also evaluate transfer learning across domains alongside a general-domain baseline (BERT-CRF). Overall, our findings highlight the efficiency of our strategies and their implications for literary NER tasks. Furthermore, experimental results reveal the adapted and domain-specific models outperform the generic baseline with an F1 score of over 75% in a strict evaluation scenario and over 80% in a partial scenario.
Keywords: named entity recognition; adaptive pre-training; literature in portuguese