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 …

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies
biomedical entities with special meanings, such as people, places, and organizations, as …

Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …

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 …

Most of the recently proposed neural models for named entity recognition have been purely
data-driven, with a strong emphasis on getting rid of the efforts for collecting external …

The dominant approaches for named entity recognitionm (NER) mostly adopt complex
recurrent neural networks (RNN), eg, long-short-term-memory (LSTM). However, RNNs are …

With social media posts tending to be multimodal, Multimodal Named Entity Recognition
(MNER) for the text with its accompanying image is attracting more and more attention since …

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 …

Abstract Although the existing Named Entity Recognition (NER) models have achieved
promising performance, they suffer from certain drawbacks. The sequence labeling-based …