Recognizing UMLS semantic types with deep learning
| Autor: | Berry de Bruijn, Khaldoun Zine El Abidine, Isar Nejadgholi, Astha LaPlante, Muqun Li, Kathleen C. Fraser |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
business.industry Computer science Deep learning Unified Medical Language System Contrast (statistics) 010501 environmental sciences computer.software_genre 01 natural sciences Relationship extraction Task (project management) 03 medical and health sciences Entity linking Artificial intelligence business computer Natural language processing Word (computer architecture) 030304 developmental biology 0105 earth and related environmental sciences |
| Zdroj: | LOUHI@EMNLP |
| Popis: | 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. Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), Nov. 3rd, 2019, Hong Kong |
| Databáze: | OpenAIRE |
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