Deep neural models for named entity recognition (NER) have shown impressive results in
overcoming label scarcity and generalizing to unseen entities by leveraging distant …

Training neural models for named entity recognition (NER) in a new domain often requires
additional human annotations (eg, tens of thousands of labeled instances) that are usually …

To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER
methods are widely applied to automatically label data and identify entities. Although the …

Deep neural network models have helped named entity (NE) recognition achieve amazing
performance without handcrafting features. However, existing systems require large …

Named Entity Recognition (NER) is an essential steppingstone in the field of natural
language processing. Although promising performance has been achieved by various …

Abstract Traditional Named Entity Recognition (NER) models are typically designed for
domain-specific datasets and limited to fixed predefined types, resulting in difficulty …

Named entity recognition (NER) is a fundamental and important task in NLP, aiming at
identifying named entities (NEs) from free text. Recently, since the multi-head attention …

It has been shown that named entity recognition (NER) could benefit from incorporating the
long-distance structured information captured by dependency trees. We believe this is …

The success of neural network based methods in named entity recognition (NER) is heavily
relied on abundant manual labeled data. However, these NER methods are unavailable …

Deep learning models have achieved state-of-the-art performance in named entity
recognition (NER); the good performance, however, relies heavily on substantial amounts of …