| Challenge: | Biomedical Concept Normalization (BCN) is widely used in biomedical text processing . despite numerous surface variants of biomedically-defined concepts, it remains challenging and unsolved. |
| Approach: | They propose a framework that uses hypernyms and synonyms to facilitate BCN . they use list-wise training to make use of both hypernies and synonym entities . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on the NCBI dataset. |
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| Challenge: | Existing methods for biomedical named entity recognition require laborious human effort. |
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| Challenge: | Biomedical named entities are often used as key features in biomedical text mining. |
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A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization (2020.acl-main)
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Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra
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A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)
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| Challenge: | Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases. |
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