An ensemble CNN method for biomedical entity normalization

Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Medical Concept Normalization BB-norm-habitat PADIA wang 0.684 # 3
accuracy 0.488 # 2
Medical Concept Normalization BB-norm-phenotype PADIA wang 0.758 # 2
accuracy 0.618 # 2