CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

• URL: http://arxiv.org/abs/2112.08578v1
• Date: Thu, 16 Dec 2021 02:40:43 GMT
• Title: CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
• Authors: Swapnil Hingmire, Irene Li, Rena Kawamura, Benjamin Chen, Alexander Fabbri, Xiangru Tang, Yixin Liu, Thomas George, Tammy Liao, Wai Pan Wong, Vanessa Yan, Richard Zhou, Girish K. Palshikar, Dragomir Radev
• Abstract summary: A classification scheme of a scientific subject gives an overview of its body of knowledge. 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.
• Score: 47.48935730905393
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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