  The field of Natural Language Processing (NLP) is growing rapidly, with new
research published daily along with an abundance of tutorials, codebases and
other online resources. In order to learn this dynamic field or stay up-to-date
on the latest research, students as well as educators and researchers must
constantly sift through multiple sources to find valuable, relevant
information. To address this situation, we introduce TutorialBank, a new,
publicly available dataset which aims to facilitate NLP education and research.
We have manually collected and categorized over 6,300 resources on NLP as well
as the related fields of Artificial Intelligence (AI), Machine Learning (ML)
and Information Retrieval (IR). Our dataset is notably the largest
manually-picked corpus of resources intended for NLP education which does not
include only academic papers. Additionally, we have created both a search
engine and a command-line tool for the resources and have annotated the corpus
to include lists of research topics, relevant resources for each topic,
prerequisite relations among topics, relevant sub-parts of individual
resources, among other annotations. We are releasing the dataset and present
several avenues for further research.
