The use of deep learning for fault diagnosis is already a common approach. However,
integrating discriminative information of fault types and scales into deep learning models for …

In recent years, scholars have paid increasing attention to the joint entity and relation
extraction. However, the most difficult aspect of joint extraction is extracting overlapping …

Joint relational triple extraction treats entity recognition and relation extraction as a joint task
to extract relational triples, and this is a critical task in information extraction and knowledge …

Data augmentation technology has been widely used in computer vision and speech with
good results. In computer vision and speech, simple manipulation of gold data can achieve …

Scientific articles and reports contain various domain-specific knowledge in the form of
entities and relations between them. In recent years, such knowledge including overlapping …

实体关系三元组的抽取效果直接影响后期知识图谱构建的质量, 而传统流水线式和联合式抽取的
模型, 并没有对句子级别和关系级别的语义特征进行有效建模, 从而导致模型性能的缺失. 为此 …

Entity and relation extraction (ERE) is a core task in information extraction. This task has
always faced the overlap problem. It was found that heterogeneous graph attention networks …

Extracting entity, relation, and attribute information from unstructured text is crucial for
constructing large-scale knowledge graphs (KG). Existing research approaches either focus …

Recent methods of extracting relational triples mainly focus on the overlapping problem and
achieve considerable performance. Most previous approaches extract triples solely …

Relation extraction aims to identify semantic relationships between entities from the given
sentences. The development of deep learning prompts a variety of neural-based relation …