Machine Learning & Natural Language Processing

 

  • 🐷 := fundamental theories
  • 👨‍👩‍👧‍👦 := series of papers on a same topic
  • 🐶 := a single paper

 

1 Maths

1.1 Linear Algebra & Functional Analysis

1.2 Probability & Statistics

1.3 Optimization & Numerical Computation

 

2 ML

2.1 Fundamentals

2.2 Deep Clustering & Subspace Clustering

2.3 Spectral Methods & GNN

2.4 Few-shot Learning

2.4.1 Metric Learning

2.4.2 Meta Optimizer Based

2.4.3 Improvements

2.5 Others

 

3 NLP

3.1 Fundamentals

3.2 Information Extraction

3.2.1 Sequence Labelling

3.2.2 Named Entity Recognition

3.2.3 Relation Extraction

– Few-Shot RE
– Document RE
– Dialog RE
– Others

3.2.4 Entity Relation Extraction

3.2.5 Event Extraction

3.2.6 Intent Detection

3.3 Representation Learning in NLP

3.4 Knowledge Graph

3.4.1 Reasoning on Graph

3.5 Pretraining

3.6 Others

4 Others