1. Introduction
1.1. Datasets for Relation Extraction
1.2. Relation Extraction: Global Level vs Mention Level
1.3. Previous Surveys
2. Supervised Approaches
2.1. Feature-based Methods
2.2. Kernel Methods
2.3. Evaluation
3. Joint Extraction of Entities and Relations
3.1. Integer Linear Programming Based Approach
3.2. Graphical Models Based Approach
3.3. Card-Pyramid Parsing
3.4. Structured Prediction
4. Semi-supervised Approaches
4.1. Bootstrapping Approaches
4.2. Active Learning
4.3. Label Propagation Method
4.4. Other Methods
4.5. Evaluation
5. Unsupervised Relation Extraction
5.1. Clustering Based Approaches
5.2. Other Approaches
6. Open Information Extraction
7. Distant Supervision
8. Recent Advances in Relation Extraction
9. Conclusion and Future Research Directions