LectureBank: a dataset for NLP Education and Prerequisite Chain Learning


In this blog post, we introduce our AAAI 2019 accepted paper “What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning.”
Our LectureBank dataset contains 1,352 English lecture files collected from university courses in mainly Natural Language Processing (NLP) field. Besides, each file is manually classified according to an existing taxonomy. Together with the dataset, we include 208 manually-labeled prerequisite relation topics. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

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