# An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention
## 5 Conclusions and Future Work  
Entity linking has received increasing attention as a fundamental task for various types of medical natural language processing tasks. In order to address the challenge of large numbers of parameters in large pre-trained models, the long inference time, and the difficulty of obtaining good results with a single matching model due to the excessive variety of biomedical mention representations, in this paper, we construct an efficient biomedical entity linking method that incorporates inter- and intra-entity attention in a unified model to better capture information between biomedical mentions and candidate entities themselves as well as between each other. The model in this paper is also more lightweight. We have systematically studied the influence of our idea and carried out experiments. Furthermore, we also designed a biomedical entity linking method based on BERT and pairwise ranking to compare with the lightweight method in this paper. Experimental results demonstrate that the proposed method in this paper achieves fairly competitive performance on two biomedical benchmark datasets. Furthermore, 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. The results demonstrate the effectiveness of our model by achieving significant performance. The biomedical entity linking method proposed in this paper can solve the problems in entity linking, but there are still some limitations, which will be addressed in future work. The specific shortcomings and improvement measures are as follows:  
Firstly, the recall rate of the candidate phase directly determines the accuracy of the candidate ranking phase. It is worthwhile to further improve the upper bound of the ranking system. In addition, the task in this paper can also include features such as prior information, contextual information, and coherence information, and it is expected that the additional inclusion of this part of information can further improve the effect, which is to be further studied subsequently. Finally, the analysis of the incorrectly predicted entity mentions reveals that the ranking model is inaccurate in predicting the presence of a mention corresponding to more than one criterion word, for which a new model can be designed in future work to handle the prediction task of a mention corresponding to multiple criterion candidates. Conceptualization, M.A. and T.T. ; methodology, M.A. and T.T. ; software, M.A. ; validation, M.A. and T.T. ; formal analysis, M.A., T.T. and A.H.; investigation, M.A. and T.T. ; data curation, M.A. ; writing--original draft preparation, M.A. ; writing--review and editing, M.A., T.T. and A.H.; visualization, M.A. ; supervision, T.T. and A.H.; project administration, T.T. and A.H.; funding acquisition, T.T. and A.H. All authors have read and agreed to the published version of the manuscript. This work has been supported by the National Natural Science Foundation of China (62166042, U2003207), Natural Science Foundation of Xinjiang, China (2021D01C076), and Strengthening Plan of National Defense Science and Technology Foundation of China (2021-JCJCJ-J0059). Not applicable. The dataset supporting the conclusions of this article is available at [[https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/)]([https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/)) and [[https://bionlp.nlm.nih.gov/tac2017](https://bionlp.nlm.nih.gov/tac2017) adversereactions/]([https://bionlp.nlm.nih.gov/tac2017](https://bionlp.nlm.nih.gov/tac2017) adversereactions/), accessed on 21 December 2021.