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).
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Ranked #1 on
Named Entity Recognition
on ACE 2005
(using extra training data)
CHINESE NAMED ENTITY RECOGNITION ENTITY EXTRACTION USING GAN MACHINE READING COMPREHENSION NESTED MENTION RECOGNITION NESTED NAMED ENTITY RECOGNITION
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.
Ranked #2 on
Nested Mention Recognition
on ACE 2005
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.
Ranked #8 on
Named Entity Recognition
on GENIA
ENTITY LINKING NESTED MENTION RECOGNITION NESTED NAMED ENTITY RECOGNITION RELATION EXTRACTION
When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.
Ranked #1 on
Nested Named Entity Recognition
on ACE 2004
(using extra training data)
We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels.
Ranked #8 on
Named Entity Recognition
on GENIA
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Ranked #3 on
Nested Mention Recognition
on ACE 2005
ENTITY EMBEDDINGS NESTED MENTION RECOGNITION NESTED NAMED ENTITY RECOGNITION
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.
Ranked #6 on
Nested Mention Recognition
on ACE 2005
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.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
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.
Ranked #4 on
Named Entity Recognition
on ACE 2004
MULTI-GRAINED NAMED ENTITY RECOGNITION NESTED MENTION RECOGNITION NESTED NAMED ENTITY RECOGNITION
It is common that entity mentions can contain other mentions recursively.
Ranked #6 on
Nested Mention Recognition
on ACE 2004