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In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.<\/jats:p>","DOI":"10.3233\/sw-223177","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T11:08:34Z","timestamp":1660043314000},"page":"239-260","source":"Crossref","is-referenced-by-count":5,"title":["Generation of training data for named entity recognition of artworks"],"prefix":"10.1177","volume":"14","author":[{"given":"Nitisha","family":"Jain","sequence":"first","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Sierra-M\u00fanera","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, 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