Dependency tree structures capture long-distance and syntactic relationships between
words in a sentence. The syntactic relations (eg, nominal subject, object) can potentially …

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 …

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

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 …

Named entity recognition (NER) isa preliminary task in natural language processing (NLP).
Recognizing Chinese named entities from unstructured texts is challenging due to the lack …

Current state-of-the-art approaches for named entity recognition (NER) typically consider
text at the sentence-level and thus do not model information that crosses sentence …

Named entity recognition (NER), which focuses on the extraction of semantically meaningful
named entities and their semantic classes from text, serves as an indispensable component …

Named entity recognition (NER) models are typically based on the architecture of Bi-
directional LSTM (BiLSTM). The constraints of sequential nature and the modeling of single …

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic
properties where entities may be extracted according to how they are used and placed in the …

Named entity recognition (NER) is the task to detect and classify the entity spans in the text.
When entity spans overlap between each other, this problem is named as nested NER …