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The field of Natural Language Processing (NLP) is growing rapidly, with newresearch published daily along with an abundance of tutorials, codebases andother online resources. In order to learn this dynamic field or stay up-to-dateon the latest research, students as well as educators and researchers mustconstantly sift through multiple sources to find valuable, relevantinformation. 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 wellas the related fields of Artificial Intelligence (AI), Machine Learning (ML)and Information Retrieval (IR). Our dataset is notably the largestmanually-picked corpus of resources intended for NLP education which does notinclude only academic papers. Additionally, we have created both a searchengine and a command-line tool for the resources and have annotated the corpusto include lists of research topics, relevant resources for each topic,prerequisite relations among topics, relevant sub-parts of individualresources, among other annotations. We are releasing the dataset and presentseveral avenues for further research.

TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation