Nested Mention Recognition
10 papers with code • 2 benchmarks • 4 datasets
Nested mention recognition is the task of correctly modeling the nested structure of mentions.
Latest papers
Bipartite Flat-Graph Network for Nested Named Entity Recognition
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers.
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.
Neural Architectures for Nested NER through Linearization
We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label.
Merge and Label: A novel neural network architecture for nested NER
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
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.
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.
A Neural Transition-based Model for Nested Mention Recognition
It is common that entity mentions can contain other mentions recursively.
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 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.