Nested Named Entity Recognition

44 papers with code • 6 benchmarks • 11 datasets

Nested named entity recognition is a subtask of information extraction that seeks to locate and classify nested named entities (i.e., hierarchically structured entities) mentioned in unstructured text (Source: Adapted from Wikipedia).

Most implemented papers

A Unified MRC Framework for Named Entity Recognition

ShannonAI/mrc-for-flat-nested-ner ACL 2020

Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.

Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

yahshibu/nested-ner-tacl2020-transformers 5 Sep 2019

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.

Pyramid: A Layered Model for Nested Named Entity Recognition

LorrinWWW/Pyramid ACL 2020

Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.

DiffusionNER: Boundary Diffusion for Named Entity Recognition

tricktreat/diffusionner 22 May 2023

In this paper, we propose DiffusionNER, which formulates the named entity recognition task as a boundary-denoising diffusion process and thus generates named entities from noisy spans.

A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign

minhpqn/vietner 22 Mar 2018

In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign.

A Neural Layered Model for Nested Named Entity Recognition

meizhiju/layered-bilstm-crf NAACL 2018

Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer.

Neural Segmental Hypergraphs for Overlapping Mention Recognition

berlino/overlapping-ner-em18 EMNLP 2018

In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets.

A Neural Transition-based Model for Nested Mention Recognition

berlino/nest-trans-em18 EMNLP 2018

It is common that entity mentions can contain other mentions recursively.

Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks

sanmusunrise/ARNs ACL 2019

In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i. e., although a mention can nest other mentions, they will not share the same head word.

Multi-Grained Named Entity Recognition

congyingxia/Multi-Grained-NER ACL 2019

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.