B io FEG : Generate Latent Features for Biomedical Entity Linking (2023)

Abstract: Biomedical entity linking is an essential task in biomedical text processing, which aims to map entity mentions in biomedical text, such as clinical notes, to standard terms in a given knowledge base. However, this task is challenging due to the rarity of many biomedical entities in real-world scenarios, which often leads to a lack of annotated data for them. Limited by understanding these unseen entities, traditional biomedical entity linking models suffer from multiple types of linking errors. In this paper, we propose a novel latent feature generation framework BioFEG to address these challenges. Specifically, our BioFEG leverages domain knowledge to train a generative adversarial network, which generates latent semantic features of corresponding mentions for unseen entities. Utilizing these features, we fine-tune our entity encoder to capture fine-grained coherence information of unseen entities and better understand them. This allows models to make linking decisions more accurately, particularly for ambiguous mentions involving rare entities. Extensive experiments on the two benchmark datasets demonstrate the superiority of our proposed framework.

## introduction biomedical entity linking assigns biomedical entity mentions in texts to corresponding canonical concepts in a knowledge base, is a key task in the biomedical nlp area. this task plays a vital role in bridging unstructured text and structured biomedical knowledge bases, making it an essential component in several downstream medical-related applications, including knowledge discovery and medical diagnosis @xcite , high-throughput phenotyping @xcite , literature searching @xcite and biomedical question answering @xcite . although many previous biomedical entity linking methods @xcite figure 1 : the motivation illustration of our proposed biofeg. linking errors may occur if unseen entities are not sufficiently understood. our biofeg generates latent semantic features of corresponding mentions for unseen entities to fine-tune entity encoders to capture fine-grained coherence signals of unseen entities. due to the limited understanding of unseen entities, there are three types of linking errors for biomedical entity linking, as illustrated in figure 1a . the first type involves linking mentions of unseen entities to wrong seen entities, as in the top case where the mention "detoxification" may be linked to the wrong entity "liver detoxification". the second type is linking mentions of seen entities to wrong unseen entities, like in the middle case where the mention "sagittal" is confused with unseen entities "sagittal sinus" and "sagittal stratum". the most challenging type is the third type, which occurs when linking mentions of unseen entities to wrong unseen entities. as in the bottom case, the mention "right knee" can not be correctly linked without sufficiently understanding both "lateral part of right knee" and "structure of right knee". some previous works @xcite have realized the challenge of unseen entities. they cluster mentions by utilizing mention-mention coreference relationships to better disambiguate mentions. however, considering the rarity of unseen entities, most clusters of them only contain few mentions. this makes it difficult to utilize mention-mention relationships of unseen entities. thus, these methods are good at managing the second type of linking error linking mentions of seen entities to wrong unseen entities but still seem troubling to deal with the first and third type of linking errors. to address these problems, we propose a novel latent feature generation framework for biomedical entity linking in this paper, biofeg in short. our biofeg is an iterative framework and each iteration contains three steps, which is shown in figure 1b . in the first step, we train a biomedical entity linking retriever using the training set consisting of the training pairs of seen entities. to better address the second type of linking error, we utilize hard negative sampling there, which is simple but effective. in the second step, we leverage domain knowledge of seen entities to train a generative adversarial network (gan) @xcite to learn to generate corresponding mention latent semantic features. in the third step, we fine-tune the entity encoder of our retriever by utilizing the generated latent features of unseen entities. this captures fine-grained coherence signals of unseen entities to simultaneously handle all three types of linking errors. the main contributions of this paper are summarized as follows: experimental results demonstrate the superiority of our proposed framework. ## analysis 5.1 ablation studies to gain a better understanding of our proposed biofeg, we conduct a series of ablation studies, as presented in ## conclusion in this paper, we focus on rare entities in biomedical entity linking. to address the challenge of lacking training data and insufficiently understanding of unseen entities, we propose biofeg, a novel framework to generate pseudo data in the latent feature space for unseen entities to fine-tune the entity encoder of our retriever. this captures more finegrained coherence signals of unseen entities, allowing for simultaneously handling all the three types of linking errors caused by insufficiently understanding of unseen entities. experimental results on two public biomedical entity linking datasets demonstrate the effectiveness of utilizing generated latent semantic features and our proposed biofeg achieves state-of-the-art performance.