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 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 …

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 …

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 …

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 …

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 well-studied task in natural language processing.
Traditional NER research only deals with flat entities and ignores nested entities. The span …

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 …

Abstract Modern Natural Language Processing (NLP) makes intensive use of deep learning
methods because of the accuracy they offer for a variety of applications. Due to the …

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 …