BioNER-CFEM: Biomedical Named Entity Recognition Based on Character Feature Enhancement with Multimodal Method

Biomedical named entity recognition (Bio-NER) is an essential task for biomedical information extraction. In this paper, we regard word-level features and character-level features as two different modalities from a novel perspective and propose a biomedical named entity recognition model based on character feature enhancement with multimodal method (called BioNER-CFEM). BioNER-CFEM can not only capture interactions between modalities, but also learn interactions within modalities. In addition, our proposed cross-attention based sparse selection mechanism can effectively alleviate the noise in the interaction process of the two ‘modalities’. Experimental results show the effectiveness of BioNER-CFEM for the Bio-NER task: it achieves performance boost over SOTA models with competitive efficiency on all six Bio-NER datasets, i.e., $+0.89, +0.64, +0.40$, $+1.40, +5.57, +2.81$ on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chem, BC2GM, JNLPBA, BC4CHEMD, respectively.