Currently, medical institutes generally use EMR to record patient's condition, including
diagnostic information, procedures performed, and treatment results. EMR has been …

Background Temporal relations between clinical events play an important role in clinical
assessment and decision making. Extracting such relations from free text data is a …

In this paper, we present the C hinese Me dical I nformation E xtraction (CMeIE) dataset,
consisting of 28, 008 sentences, 85, 282 triplets, 11 entities, and 44 relations derived from …

Medical text classification assigns medical related text into different categories such as
topics or disease types. Machine learning based techniques have been widely used to …

Temporal relation extraction between health-related events is a widely studied task in
clinical Natural Language Processing (NLP). The current state-of-the-art methods mostly rely …

Extracting relational triples from unstructured medical texts can provide a basis for the
construction of large-scale medical knowledge graphs. The cascade binary pointer tagging …

Recent years have witnessed the rapid development and tremendous research interests in
healthcare domain. The health and medical knowledge can be acquired from many sources …

实体抽取和关系抽取作为信息抽取的重要子任务, **些年众多学者利用多种技术在该领域开展了
深入研究. 将这些技术应用于医学领域, 抽取非结构化和半结构化的医学文本构建医学知识图谱 …

[目的] 针对当前传统医学关系抽取方法存在数据标注成本高及易产生错误标签的问题,
提出引入医学领域知识的远程监督医学实体关系抽取模型.[方法] 该模型采用多实例策略降低 …

This paper studies the differences between different types of newspapers in expressing
temporal information, which is a topic that has not received much attention. Techniques from …