Concept
Improved Concept Embeddings for Learning Prerequisite Chains: Poincaré embeddings
- The embedding models using the Euclidean vector space are unable to capture hierarchical relationships, which is not optimized for embedding structures like graphs and trees. Nickel and Kiela (2017) proposed a model that embeds concepts into a hyperbolic space, which can be thought of as a continuous version of trees structure and therefore provides a better fit to embed data with latent hierarchies.
- Poincaré ball model represents the entire hyperbolic space within the border of an n-dimensional sphere. This model, referred to as Poincaré Embeddings, generates the embeddings having as its input a weighted directed graph , in which each node represents a concept and a directed edge between a and b means that a is a hypernym of b, in other words, b is a prerequisite concept of a.
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Updated 2020-08-04
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Data Science
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Improved Concept Embeddings for Learning Prerequisite Chains: Poincaré embeddings
Improved Concept Embeddings for Learning Prerequisite Chains: Generating Poincaré embeddings from natural-language text corpora
Improved Concept Embeddings for Learning Prerequisite Chains: Model parameters
Improved Concept Embeddings for Learning Prerequisite Chains: Evaluation methods