Resumen
Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inference time. In addition, due to the numerous surface variants of biomedical mentions, it is difficult for a single matching module to achieve good results. To address the challenge, we propose an efficient biomedical entity linking method that integrates inter- and intra-entity attention to better capture the information between medical entity mentions and candidate entities themselves and each other, and the model in this paper is more lightweight. Experimental results show that our method achieves competitive performance on two biomedical benchmark datasets, NCBI and ADR, with an accuracy rate of 91.28% and 93.13%, respectively. Moreover, it also achieves comparable or even better results compared to the BERT-based entity linking method while having far fewer model parameters and very high inference speed.