Our LectureBank dataset contains 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 322 manually-labeled prerequisite relation topics. Check our most recent paper "R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning" (see GitHub) by Irene Li, Alexander Fabbri, Swapnil Hingmire, Dragomir Radev.
NLP is rapidly growing, and, as a result, advancing in the field can seem daunting to the student or the researcher. To help the growing NLP community and advance research related to NLP for educational applications, we introduced a new corpus in our ACL 2018 paper "TutorialBank: Using a Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation" (see GitHub) by Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Wei Tai Ting, Robert Tung, Caitlin Westerfield and Dragomir Radev.
We release a new paper "CLICKER: A Computational LInguistics Classification Scheme for Educational Resources" by the LILY group at Yale!
We are pleased to release the new, 2022 version of the AAN database with over 24,000 resources and over 7,000 lecture notes! Please check out our blog post here for more information.
|Number of papers||24622|
|Number of authors||18904|
|Number of venues||374|
|Number of citations||124884|
|Number of resources||24836|