|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
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.
#4 best model for Named Entity Recognition on GENIA
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
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
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
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.
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
When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.
#2 best model for Nested Named Entity Recognition on ACE 2004