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 …

After decades of massive digitisation, an unprecedented number of historical documents are
available in digital format, along with their machine-readable texts. While this represents a …

There is a recent interest in investigating few-shot NER, where the low-resource target
domain has different label sets compared with a resource-rich source domain. Existing …

We present a simple few-shot named entity recognition (NER) system based on nearest
neighbor learning and structured inference. Our system uses a supervised NER model …

Multi-modal named entity recognition (MNER) aims to discover named entities in free text
and classify them into pre-defined types with images. However, dominant MNER models do …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts.
Existing approaches for MNER mainly suffer from two drawbacks:(1) despite generating …

Research on overlapped and discontinuous named entity recognition (NER) has received
increasing attention. The majority of previous work focuses on either overlapped or …

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed
for quick implementation of different neural sequence labeling models with a CRF inference …

Multimodal Named Entity Recognition (MNER) aims to locate and classify named entities
mentioned in a (text, image) pair. However, dominant work independently models the …