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

Named entity recognition (NER) is a fundamental task in the natural language processing
(NLP) area. Recently, representation learning methods (eg, character embedding and word …

Named entity recognition (NER) is crucial in various natural language processing (NLP)
tasks. However, the nested entities which are common in practical corpus are often ignored …

In the era of information explosion, named entity recognition (NER) has attracted
widespread attention in the field of natural language processing, as it is fundamental to …

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 …

In natural language processing, it is common that many entities contain other entities inside
them. Most existing works on named entity recognition (NER) only deal with flat entities but …

Named entity recognition (NER) is one of the widely studied natural language processing
tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem …

Abstract Named Entity Recognition (NER) has so far evolved from the traditional flat NER to
overlapped and discontinuous NER. They have mostly been solved separately, with only …

Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a
precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the …

Abstract Named Entity Recognition (NER) aims to extract structured entity information from
unstructured textual data by identifying entity boundaries and categories. Chinese NER is …