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).

Paper: https://www.aaai.org/ojs/index.php/AAAI/article/view/4638

Github: https://github.com/Yale-LILY/LectureBank

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning: Reference

With the help of TutorialBank (Fabbri et al. 2018) and two annotators, the authors created their own dataset, LectureBank. They used Doc2Vec model to train the datasets to topic representations that used as input to prerequisite relations learning models.

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning: Methods

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning: Results

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.

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

 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.

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