Nested mention recognition is the task of correctly modeling the nested structure of mentions.
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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
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)
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 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
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Ranked #3 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
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 work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets.
Ranked #5 on Named Entity Recognition on ACE 2004
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