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 320 manually-labeled prerequisite relation topics. Check our most recent paper "R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning" 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.
The paper "TutorialBank: Using a Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation" by the LILY group at Yale - Computer Science will be published in ACL 2018!
After several years of dormancy, AAN updates are back with the 2014 release.
|Number of authors||18904|
|Number of venues||374|
|Number of citations||124884|
|Number of papers||24622|
|Number of resources||23193|