Potential Research Concepts for the NER Task

Architecture

arch-rnn: Recurrent Neural Networks (LSTMs, GRU)
arch-cnn: Convolutional Neural Networks
arch-transformer: Transformer
arch-gnn: Graph Neural Networks (GNN)
arch-att: Attention Mechasnim
arch-crf: Conditional Random Field
arch-semicrf: Semi-Conditional Random Field

Training

train-multitask: Multi-task learning
train-multimodal: Multi-modal learning
train-auxiliary: Joint training
train-transfer: Cross-domain learning, Transfer or domain adaptation
train-multiling: Bi-lingual, Multi-lingual learning
train-active: Active learning, Bootstrapping
train-adver: Adversarial learning

Pre-trained Models

pre-train: Pre-trained, Contextualized (BERT)

Task Setting

task-nested: Nested or Multi-grained NER
task-hetero: Heterogeneous categories
task-lowres: Low-resource or Zero-resource learning
task-weaksup: Weakly Supervised NER
task-disc: Discontinuous NER

Dataset

dataset: Constructing a new dataset

Interpretable Analysis

interpret-genz: Generalization
interpret-model: System-wise Analysis
interpret-error: Annotation Errors Analysis