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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.
#5 best model for Nested Named Entity Recognition on GENIA
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
#3 best model for Nested Mention Recognition on ACE 2005
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.
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.
#4 best model for Nested Named Entity Recognition on GENIA
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.
#3 best model for Named Entity Recognition on ACE 2004
In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets.
#3 best model for Nested Named Entity Recognition on GENIA
When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.