Abstract
Many entity recognition approaches classify recognised entities into a limited set of coarse-grained entity types. However, for deeper natural language analysis and end-user tasks, fine-grained entity types are more useful. For example, while standard named entity recognition may determine that an entity is a person knowing whether that entity is a politician or an actor is important for determining whether, in a subsequent relation extraction task, a relation should be acts or governs. Currently, fine-grained entity typing has only been investigated for English. In this paper, we present a fine-grained entity typing system for Dutch and Spanish using training data extracted from Wikipedia and DBpedia. Our system achieves comparable performance to English with an F\(_{1}\) measure of .90 on over 40 types for both Dutch and Spanish.
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Notes
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Using the wikilinks and instance types dumps from the latest DBpedia, version 2016-04 http://wiki.dbpedia.org/downloads-2016-04.
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The types we could not map were the following: location/structure/government, organization/stock_exchange, other/health, other/living_thing, other/product/car, other/product/computer, person/education, person/education/student, person/education/teacher.
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Although there is more text in the Spanish DBpedia, we only included a sample here to showcase the adaptability of the approach to other languages.
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If an entity X has types location/structure and organisation/education assigned to it, two instances are generated namely X, location/structure and X, organisation/education.
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The number of types from levels 1–3 do not add up to the total number of types as some of the higher level types are not present on their own, such as other.
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dbpedia: is shorthand for http://dbpedia.org/resource.
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dbo: is shorthand for http://dbpedia.org/ontology/.
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The research for this paper was made possible by the CLARIAH-CORE project financed by NWO.
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Appendix A: Results
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van Erp, M., Vossen, P. (2017). Multilingual Fine-Grained Entity Typing. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_23
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