Enhancing Domain-Specific Named Entity Recognition via Segmentation and Pseudo-Labeled Annotation

Named Entity Recognition (NER) in specialized domains poses major challenges due to the scarcity of annotated data and the limitations of existing models. One challenge lies in handling long documents, which often requires segmenting the text into smaller chunks that fit within the model's input window. Although several segmentation strategies exist, their impact on NER performance in new domains remains underexplored. Another challenge is domain adaptation: while openschema NER models can perform reasonably well under zero-shot settings, they often struggle in highly specialized contexts without additional supervision. To address these issues, we combine segmentation strategy selection with a semi-supervised finetuning pipeline based on pseudo-labeled annotations. First, we compare four segmentation strategies to identify which offers the best trade-off between precision and recall in zero-shot settings. Then, we fine-tune two open-schema NER models-GLiNER and NuNER-first on a manually annotated dataset, and subsequently on a large pseudo-labeled dataset built from model agreement. Both models are evaluated on a held-out test set and on the full manually annotated corpus. Experiments on French-language documents specifically focused on the underexplored domain of territorial food systems reveal that fine-tuning on pseudo-labeled data-obtained through cross-model agreement-yields better performance than relying solely on human-annotated data. The results also highlight the strengths and weaknesses of different segmentation strategies and confirm the importance of optimizing segmentation choices for NER in domain-specific low-resource settings. Code related to this work is available on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/ibzodiaz/segmentation-strategies.