Objective This study aims to review the recent advances in community challenges for
biomedical text mining in China. Methods We collected information of evaluation tasks …

In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for
storing relationships between entities and for performing higher level reasoning. KGs in …

Recently, the increasing demand for superior medical services has highlighted the
discrepancies in the medical infrastructure. With big data, especially texts, forming the …

Interactions between entities in knowledge graph (KG) provide rich knowledge for language
representation learning. However, existing knowledge-enhanced pretrained language …

Background: Stroke is a prevalent disease with a significant global impact. Effective
assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and …

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset
focusing on tail and emerging entities in languages other than English. We present Hansel …

Medical named entity recognition (NER), a fundamental task of medical information
extraction, is crucial for medical knowledge graph construction, medical question answering …

Traditional clinical named entity recognition methods fail to balance the effectiveness of
feature extraction of unstructured text and the complexity of neural network models. We …

The field of biomedical research has witnessed a significant increase in the accumulation of
vast amounts of textual data from various sources such as scientific literatures, electronic …

Online medical consultation (OMC) restricts doctors to gathering patient information solely
through inquiries, making the already complex sequential decision-making process of …