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).
Datasets
Most implemented papers
A Unified MRC Framework for Named Entity Recognition
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
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
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
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
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
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
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
It is common that entity mentions can contain other mentions recursively.
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
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
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.