SrpCNNeL: Serbian Model for Named Entity Linking

Abstract

This paper presents the development of a Named Entity Linking (NEL) model to the Wikidata knowledge base for the Serbian language, named SrpCNNeL. The model was trained to recognize and link seven different named entity types (persons, locations, organizations, professions, events, demonyms, and works of art) on a dataset containing sentences from novels, legal documents, as well as sentences generated from the Wikidata knowledge base and the Leximirka lexical database. The resulting model demonstrated robust performance, achieving an F1 score of 0.8 on the test set. Considering that the dataset contains the highest number of locations linked to the knowledge base, an evaluation was conducted on an independent dataset and compared to the baseline Spacy Entity Linker for locations only