CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

TL;DR

CLICKER is a new classification scheme for computational linguistics and NLP, derived from university course lectures, aimed at improving access, organization, and educational applications of resources in the field.

Contribution

It introduces a comprehensive, lecture-based taxonomy for CL/NLP, filling the gap of existing classification systems like ACM CCS and MSC.

Findings

Includes 334 topics focused on educational aspects

Facilitates resource retrieval and recommendation

Supports prerequisite chain learning and survey generation

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.