{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tutorialbank-a-manually-collected-corpus-for","title":"TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation","arxiv_id":"1805.04617","date":"2018-05-11","proceeding":"ACL 2018 7","authors":["Alexander R. Fabbri","Irene Li","Prawat Trairatvorakul","Yijiao He","Wei Tai Ting","Robert Tung","Caitlin Westerfield","Dragomir R. Radev"],"abstract":"The field of Natural Language Processing (NLP) is growing rapidly, with new\nresearch published daily along with an abundance of tutorials, codebases and\nother online resources. In order to learn this dynamic field or stay up-to-date\non the latest research, students as well as educators and researchers must\nconstantly sift through multiple sources to find valuable, relevant\ninformation. To address this situation, we introduce TutorialBank, a new,\npublicly available dataset which aims to facilitate NLP education and research.\nWe have manually collected and categorized over 6,300 resources on NLP as well\nas the related fields of Artificial Intelligence (AI), Machine Learning (ML)\nand Information Retrieval (IR). Our dataset is notably the largest\nmanually-picked corpus of resources intended for NLP education which does not\ninclude only academic papers. Additionally, we have created both a search\nengine and a command-line tool for the resources and have annotated the corpus\nto include lists of research topics, relevant resources for each topic,\nprerequisite relations among topics, relevant sub-parts of individual\nresources, among other annotations. We are releasing the dataset and present\nseveral avenues for further research.","url_abs":"http://arxiv.org/abs/1805.04617v1","url_pdf":"http://arxiv.org/pdf/1805.04617v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[{"slug":"tutorialbank","name":"TutorialBank","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.04617","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}