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

University of Michigan - Ann Arbor

 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.

Improved Concept Embeddings for Learning Prerequisite Chains

- 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. 

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