With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

Named Entity Recognition (NER) is the task of identifying spans that represent entities in
sentences. Whether the entity spans are nested or discontinuous, the NER task can be …

The task of named entity recognition (NER) is normally divided into nested NER and flat
NER depending on whether named entities are nested or not. Models are usually separately …

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing,
concerned with identifying spans of text expressing references to entities. NER research is …

Named entity recognition (NER) is a well-studied task in natural language processing.
Traditional NER research only deals with flat entities and ignores nested entities. The span …

Named entity recognition (NER) task aims at identifying entities from a piece of text that
belong to predefined semantic types such as person, location, organization, etc. The state-of …

Entity mentions embedded in longer entity mentions are referred to as nested entities. Most
named entity recognition (NER) systems deal only with the flat entities and ignore the inner …

We propose a simple deep neural model for nested named entity recognition (NER). Most
NER models focused on flat entities and ignored nested entities, which failed to fully capture …

This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition
(nested NER). In our approach, token or text region embeddings are recursively inputted into …

We propose a novel recurrent neural network-based approach to simultaneously handle
nested named entity recognition and nested entity mention detection. The model learns a …