Abstract
Motivated by the need of annotated data for training named entity recognition models, in this work we present our experiments on a distantly supervised approach using domain specific vocabularies and raw texts in the same domain. In the experiments we use MeSH vocabulary and a random sample of PubMed articles to automatically create an annotated corpus and train a named entity recognition model capable to identify diseases in raw text. We evaluate method against the manually curated CoNLL-2003 corpus and the NCBI-disease corpus.
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Acknowledgements
This work has been partially funded by the project LYNX which has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement no. 780602, see http://www.lynx-project.eu.
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Karampatakis, S., Dimitriadis, A., Revenko, A., Blaschke, C. (2020). Training NER Models: Knowledge Graphs in the Loop. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_23
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