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CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

arXiv 2112.08578 ↗First posted 16 Dec 2021cs.CLcited by 0

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Abstract (arXiv)

A classification scheme of a scientific subject gives an overview of its body of knowledge. It can also be used to facilitate access to research articles and other materials related to the subject. For example, the ACM Computing Classification System (CCS) is used in the ACM Digital Library search interface and also for indexing computer science papers. We observed that a comprehensive classification system like CCS or Mathematics Subject Classification (MSC) does not exist for Computational Linguistics (CL) and Natural Language Processing (NLP). We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject. The currently proposed taxonomy includes 334 topics and focuses on educational aspects of CL/NLP; it is based primarily, but not exclusively, on lecture notes from NLP courses. We discuss how such a taxonomy can help in various real-world applications, including tutoring platforms, resource retrieval, resource recommendation, prerequisite chain learning, and survey generation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 16 Dec 2021.

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References · 11

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2018Generating Wikipedia by Summarizing Long Sequencescited by 335
  3. 2018Analysis Methods in Neural Language Processing: A Surveycited by 277
  4. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  5. 2018Unsupervised Statistical Machine Translationcited by 51
  6. 2021ExplainaBoard: An Explainable Leaderboard for NLPcited by 50
  7. 2018What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learningcited by 21
  8. 2021Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoderscited by 11
  9. 2018TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendationcited by 9
  10. 2019NLPExplorer: Exploring the Universe of NLP Paperscited by 8
  11. 2021Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chainscited by 3

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