Abstract
Named entity recognition (NER) plays an important role in many information retrieval tasks, including automatic knowledge graph construction. 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 art historical archives, the recognition of titles of artworks as named entities is of high importance. In this work, we focus on identifying mentions of artworks, e.g. paintings and sculptures, from historical archives. Current state of the art NER tools are unable to adequately identify artwork titles due to the particular difficulties presented by this domain. The scarcity of training data for NER for cultural heritage poses further hindrances. To mitigate this, we propose a semi-supervised approach to create high-quality training data by leveraging existing cultural heritage resources. Our experimental evaluation shows significant improvement in NER performance for artwork titles as compared to baseline approach.
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Notes
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Linked Open Data: http://www.w3.org/DesignIssues/LinkedData.
- 2.
OpenGLAM: http://openglam.org.
- 3.
Europeana: http://europeana.eu.
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SpaCy: https://spacy.io/, version 2.1.3.
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from the exhibition catalogue “Lukas Cranach: Gemälde, Zeichnungen, Druckgraphik” (https://digi.ub.uni-heidelberg.de/diglit/koepplin1974bd1/0084).
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We thank the Wildenstein Plattner InstituteFootnote 8 for providing the corpus used in this work.
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Jain, N., Krestel, R. (2019). Who is Mona L.? Identifying Mentions of Artworks in Historical Archives. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_10
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