Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

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

We study the open-domain named entity recognition (NER) problem under distant
supervision. The distant supervision, though does not require large amounts of manual …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …

Gazetteers were shown to be useful resources for named entity recognition (NER). Many
existing approaches to incorporating gazetteers into machine learning based NER systems …

The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports is very
important to effectively identify and quickly respond to cyber threats. However, this …

Named Entity Recognition (NER) performance often degrades rapidly when applied to target
domains that differ from the texts observed during training. When in-domain labelled data is …

We study the problem of training named entity recognition (NER) models using only distantly-
labeled data, which can be automatically obtained by matching entity mentions in the raw …

In the electronic health record, using clinical notes to identify entities such as disorders and
their temporality (eg the order of an event relative to a time index) can inform many important …