Nested mention recognition is the task of correctly modeling the nested structure of mentions.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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)
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
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)
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Ranked #3 on 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.
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
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