Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK’s superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available.

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing. Benefiting from the powerful ability of deep neural networks, WSD has achieved a great advancement in recent years. Reformulating WSD as a text span extraction task is an effective approach, which accepts a sentence context of an ambiguous word together with all definitions of its candidate senses simultaneously, and requires to extract the text span corresponding with the right sense. However, the approach merely depends on a short definition to learn sense representation, which neglects abundant semantic knowledge from related senses and leads to data-inefficient learning and suboptimal WSD performance. To address the limitations, we propose a novel WSD method with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (KELESC). Specifically, a knowledge-enhanced method is proposed to enrich semantic representation by incorporating additional examples and definitions of the related senses in WordNet. Then, in order to avoid the huge computing complexity induced by the additional information, a local self-attention mechanism is utilized to constrain attention to be local, which allows longer input texts without large-scale computing burdens. Extensive experimental results demonstrate that KELESC achieves better performance than baseline models on public benchmark datasets.