Named Entity Recognition (NER) for low-resource languages remains challenging due to limited annotated data and linguistic characteristics such as rich morphology and flexible word order. In this work, we propose a probing-based method that leverages the contextual knowledge encoded in pretrained language models to detect entities. Our approach uses a substitution strategy in which words in a sentence are replaced, one by one, with candidate entities of predefined entity types, referred to as probes. By measuring how well the probes of a certain entity type fit the surrounding context of the replaced word, we estimate the compatibility between the replaced word and the entity type. The resulting compatibility scores can be used either as a standalone zero-shot NER model or as an auxiliary feature during NER model decoding. We evaluate our method on the Latin dataset provided in the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA). Our system ranked second in the coarse-grained NER task. For the fine-grained NER task, where no training data were available, we relied exclusively on the proposed scoring method without any model training and achieved third place. These results demonstrate that contextual probing can provide an effective signal for NER in low-resource settings.

This paper considers Named Entity Linking for person mentions from classical Latin texts to a domain-specific, German language knowledge base, namely Paulys RealencyclopΣdie. Following a methodology similar to (anonymous_reference), we train a transformer-based, retrieval and ranking model (BLINK) first on a general, Wikipedia-derived dataset and subsequently on a more specific dataset, gathered from various sources, linking to our target knowledge base. Results show that while BLINK performs well on mention-entity pairs linked to entities seen during training, it performs significantly worse on mention-entity pairs linking to unseen entities. We provide a detailed error analysis, propose possible exploitation strategies for a human-in-the-loop approach, and identify directions for future improvement.

In this paper, we present a study of transformer-based Named Entity Recognition (NER) as applied to Ancient Greek texts, with an emphasis on retrieving personal names. Recent research shows that, while the task remains difficult, the use of transformer models results in significant improvements. We, therefore, compare the performance of four transformer models on the task of NER for the categories of people, locations and groups, and add an out-of-domain test set to the existing datasets. Results on this set highlight the shortcomings of the models when confronted with a random sample of sentences. To be able to more straightforwardly integrate domain and linguistic knowledge to improve performance, we narrow down our approach to the category of people. The task is simplified to a binary PERS/MISC classification on the token level, starting from capitalised words. Next, we test the use of domain and linguistic knowledge to improve the results. We find that including simple gazetteer information as a binary mask has a marginally positive effect on newly annotated data and that treebanks can be used to help identify multi-word individuals if they are scarcely or inconsistently annotated in the available training data. The qualitative error analysis identifies the potential for improvement in both manual annotation and the inclusion of domain and linguistic knowledge in the transformer models.