- Poincaré embeddings (Nickel and Kiela, 2017) is a method that better fit for data with latent hierarchies than other embedding models using the Euclidean vector space.
- Dhingra et al. (2018) introduced a method to obtain Poincaré word embeddings from natural language text.
- This paper combines the two methods above to obtain prerequisite relations between concepts from two datasets in the domain of Natural Language Processing. 

Improved Concept Embeddings for Learning Prerequisite Chains: Methods

Löffel, E. P. (2019). Improved Concept Embeddings for Learning Prerequisite Chains

Paper: https://pdfs.semanticscholar.org/6073/0900a80fcee29e9059fd293a1fa38742abf3.pdf

Github: https://github.com/eloffel/improved_embeddings

Improved Concept Embeddings for Learning Prerequisite Chains: Reference

 Löffel, E. P. (2019) generated prerequisite relations between concepts in the domain of Natural Language Processing from two datasets, TutorialBank and LectureBank, by representing natural language text corpora as Poincaré embedding.

University of Michigan - Ann Arbor

- Löffel, E. P. (2019). Improved Concept Embeddings for Learning Prerequisite Chains.
- Li, I., Fabbri, A. R., Tung, R. R., & Radev, D. R. (2019, July). What should i learn first: Introducing lecturebank for nlp education and prerequisite chain learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 6674-6681).

Examples of identifying and predicting prerequisite relationship

Improved Concept Embeddings for Learning Prerequisite Chains

Li, I., etc.(2019) introduced a new dataset, LectureBank, in 5 different domains with 208 manually-labeled prerequisite relation topics, and evaluated four binary classifiers and two Graph Convolutional Networks (GCNs) models in predicting prerequisite relationship.

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