End-to-end Biomedical Entity Linking with Span-based Dictionary Matching (2021)

Abstract: Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.

## introduction identifying disease names , which is generally called biomedical entity linking, is the fundamental process of biomedical natural language processing, and it can be utilized in applications such as a literature search system @xcite and a biomedical relation extraction @xcite . the usual system to identify disease names consists of two modules: named entity recognition (ner) and named entity normalization @xcite . ner is the task that recognizes the span of a disease name, from the start position to the end position. nen is the post-processing of ner, normalizing a disease name into a controlled vocabulary, such as a mesh or online mendelian inheritance in man (omim). although most previous studies have developed pipeline systems, in which the ner model first recognizs disease mentions @xcite ) and the nen model normalizes the recognized mention @xcite @xcite , a few approaches employ a joint learning architecture for these tasks @xcite . these joint approaches simultaneously recognize and normalize disease names utilizing their mutual benefits. for example, @xcite demonstrated that dictionary-matching features, which are commonly used for nen, are also effective for ner. while these joint learning models achieve high performance for both ner and nen, they predominately rely on hand-crafted features, which are difficult to construct because of the domain knowledge requirement. recently, a neural network (nn)-based model that does not require any hand-crafted features was applied to the joint learning of ner and nen @xcite . ner and nen were defined as two token-level classification tasks, i.e., their model classified each token into iob2 tags and concepts, respectively. although their model achieved the state-of-the-art performance for both ner and nen, a concept that does not appear in training data (i.e., zero-shot situation) can not be predicted properly. one possible approach to handle this zero-shot situation is utilizing the dictionary-matching features. suppose that an input sentence "classic polyarteritis nodosa is a systemic vasculitis" is given, where "polyarteritis nodosa" is the target entity. even if it does not appear in the training data, it can be recognized and normalized by referring to a controlled vocabulary that contains "polyarteritis nodosa (mesh: d010488)." combining such looking-up mechanisms with nn-based models, however, is not a trivial task; dictionary matching must be performed at the entity-level, whereas standard nn-based ner and nen tasks are performed at the token-level (for example, @xcite . to overcome this problem, we propose a novel end-to-end approach for ner and nen that combines dictionary-matching features with nn-based models. based on the span-based model introduced by @xcite , our model first computes span representations for all possible spans of the input sentence and then combines the dictionarymatching features with the span representations. using the score obtained from both features, it directly classifies the disease concept. thus, our model can handle the zero-shot problem by using dictionary-matching features while maintaining the performance of the nn-based models. our model is also effective in situations other than the zero-shot condition. consider the following input sentence: "we report the case of a patient who developed acute hepatitis," where "hepatitis" is the target entity that should be normalized to "drug-induced hepatitis." while the longer span "acute hepatitis" also appears plausible for standalone ner models, our end-to-end architecture assigns a higher score to the correct shorter span "hepatitis" due to the existence of the normalized term ("drug-induced hepatitis") in the dictionary. through the experiments using two major ner and nen corpora, we demonstrate that our model achieves competitive results for both corpora. further analysis illustrates that the dictionarymatching features improve the performance of nen in the zero-shot and other situations. our main contributions are twofold: (i) we propose a novel end-to-end model for disease name recognition and normalization that utilizes both nn-based features and dictionary-matching features; (ii) we demonstrate that combining dictionary-matching features with an nn-based model is highly effective for normalization, especially in the zero-shot situations. ## results & discussions comparing the model variation results, adding dictionary-matching features improved the performance in nen. the results clearly suggest that dictionary-matching features are effective for nnbased nen models. ## conclusion we proposed a end-to-end model for disease name recognition and normalization that combines the nn-based model with the dictionary-matching features. our model achieved highly competitive results for the ncbi disease corpus and bc5cdr corpus, demonstrating that incorporating dictionary-matching features into an nn-based model can improve its performance. further experiments exhibited that dictionary-matching features enable our model to accurately predict the concepts in the zero-shot situation, and they are also beneficial in the other situation. while the results illustrate the effectiveness of our model, we found several areas for improvement, such as the general terms in the dictionary and the interaction between disease names within a sentence. a possible future direction to deal with general terms is to jointly train the parameters representing the importance of each synonyms.