  Recent years have witnessed the rising popularity of Natural Language
Processing (NLP) and related fields such as Artificial Intelligence (AI) and
Machine Learning (ML). Many online courses and resources are available even for
those without a strong background in the field. Often the student is curious
about a specific topic but does not quite know where to begin studying. To
answer the question of "what should one learn first," we apply an
embedding-based method to learn prerequisite relations for course concepts in
the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English
lecture files collected from university courses which are each classified
according to an existing taxonomy as well as 208 manually-labeled prerequisite
relation topics, which is publicly available. 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.
