Recognizing UMLS Semantic Types with Deep Learning

Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine


Abstract
Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.
Anthology ID:
D19-6219
Volume:
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Month:
November
Year:
2019
Address:
Hong Kong
Venue:
Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
157–167
Language:
URL:
https://aclanthology.org/D19-6219
DOI:
10.18653/v1/D19-6219
Bibkey:
Cite (ACL):
Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, and Khaldoun Zine El Abidine. 2019. Recognizing UMLS Semantic Types with Deep Learning. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 157–167, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Recognizing UMLS Semantic Types with Deep Learning (Nejadgholi et al., Louhi 2019)
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PDF:
https://preview.aclanthology.org/author-url/D19-6219.pdf