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 …

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

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

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 highly sensitive to sentential syntactic and semantic
properties where entities may be extracted according to how they are used and placed in the …

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 …

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 …

In this paper we investigate the role of the dependency tree in a named entity recognizer
upon using a set of GCN. We perform a comparison among different NER architectures and …

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 seeks to extract substrings within a text that name real-world
objects and to determine their type (for example, whether they refer to persons or …