SrpCNNeL: Serbian Model for Named Entity Linking

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, organisations, professions, events, demonyms, and works of art) on the dataset containing sentences from novels, legal documents, as also sentences generated from the Wikidata knowledge base and 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.

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1. This research was supported by the Science Fund of the Republic of Serbia, #7276, Text Embeddings - Serbian Language Applications - TESLA.

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2. Thematic Sessions: Regular Papers