Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

Few-shot learning under the-way-shot setting (ie, annotated samples for each of classes)
has been widely studied in relation extraction (eg, FewRel) and image classification (eg …

Currently the unified semantic role labeling (SRL) that achieves predicate identification and
argument role labeling in an end-to-end manner has received growing interests. Recent …

In today's dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk
of cyberattacks are both increasing. This means that organizations need to have a strong …

A majority of research interests in irregular (eg, nested or discontinuous) named entity
recognition (NER) have been paid on nested entities, while discontinuous entities received …

Recent neural architectures in named entity recognition (NER) have yielded state-of-the-art
performance on single domain data such as newswires. However, they still suffer from (i) …

Recent neural architectures in sequence labeling have yielded state-of-the-art performance
on single domain data such as newswires. However, they still suffer from (i) requiring …

Facing the dynamic complex cyber environments, internal and external cyber threat
intelligence, and the increasing risk of cyber-attack, knowledge graphs show great …