CNN-based named entity linking: Serbian use case

Abstract This research presents the development and evaluation of Convolutional Neural Network (CNN)-based models for Named Entity Linking (NEL) in Serbian to the Wikidata knowledge base. It introduces four novel model configurations derived from two underlying Named Entity Recognition (NER) systems: the 7-class SrpCNNER2 and a specialized SrpCNNER2-loc model for locations. Different training strategies for entity linking were implemented, including training on all entity types ( srNEL-all ), the top three most frequent types ( srNEL-top3 ), locations only with NER ( srNEL-loc ), and a fully specialized location-only approach ( srNEL-LOCfull ). Models were trained on a corpus (73,493 sentences) comprising literary excerpts, legal documents, news articles, and synthetically generated sentences. The performance was evaluated on the sr-geography corpus derived from elementary school geography textbooks, using strict evaluation metrics and SrpCNNeL model as the baseline. The srNEL-all configuration, leveraging the comprehensive SrpCNNER2 NER base and trained on all entity types, achieves the highest performance on linking locations within the sr-geography corpus, attaining an F1 0.845, outperforming the SrpCNNeL baseline (F1 0.731) and models employing more specialized training regimes, such as srNEL-LOCfull (F1 0.411). The findings highlight the effectiveness of the proposed methods for Serbian NEL and underscore the benefits of incorporating broader entity context during training. While transformer-based dense-retrieval and RAG systems currently dominate multilingual entity linking research, this work prioritizes an efficient CNN-based architecture suitable for less-resource and morphologically rich languages, demonstrating competitive performance for Serbian. Although this system is faster and requires lower hardware requirements in terms of machine performance, the highest-performing model was compared with a transformer-based baseline, demonstrating its superiority over the CNN architecture. Future directions include further development of models for Serbian and exploration of hybrid architectures that combine efficiency with state-of-the-art performance.