With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

The volume of published biomedical research, and therefore the underlying biomedical
knowledge base, is expanding at an increasing rate. Among the tools that can aid …

We propose a simple deep neural model for nested named entity recognition (NER). Most
NER models focused on flat entities and ignored nested entities, which failed to fully capture …

In natural language processing, it is common that many entities contain other entities inside
them. Most existing works on named entity recognition (NER) only deal with flat entities but …

To facilitate and survey studies in automatic de-identification, as a part of the i2b2
(Informatics for Integrating Biology to the Bedside) project, authors organized a Natural …

In this work, we propose a novel segmental hypergraph representation to model overlapping
entity mentions that are prevalent in many practical datasets. We show that our model built …

Motivation Best performing named entity recognition (NER) methods for biomedical literature
are based on hand-crafted features or task-specific rules, which are costly to produce and …

Many named entities contain other named entities inside them. Despite this fact, the field of
named entity recognition has almost entirely ignored nested named entity recognition, but …

Objective The authors' goal was to develop and evaluate machine-learning-based
approaches to extracting clinical entities—including medical problems, tests, and treatments …

命名实体识别是自然语言处理任务的重要步骤. 近年来, 不依赖人工特征的神经网络在新闻等
通用领域命名实体识别方面表现了很好的性能. 然而在生物医学领域, 许多实验表明基于领域 …