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

The rapid development of data and artificial intelligence technology has introduced new
opportunities and challenges to aeronautical information intelligence. However, there are …

Natural language processing (NLP) is crucial in the current processing of data because it
takes into account many sources, formats, and purposes of data as well as information from …

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 advances in deep neural models allow us to build reliable named entity recognition
(NER) systems without handcrafting features. However, such methods require large …

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 …

For many natural language processing (NLP) tasks the amount of annotated data is limited.
This urges a need to apply semi-supervised learning techniques, such as transfer learning …

We present a bi-encoder framework for named entity recognition (NER), which applies
contrastive learning to map candidate text spans and entity types into the same vector …

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 …

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 …