Towards automatic identification of core concepts in educational resources

Md Arafat Sultan, Steven Bethard, Tamara Sumner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Automatically identifying and extracting key ideas and concepts from educational resources is an important but challenging computational task. We present a supervised machine learning approach to assessing the 'coreness' of concepts expressed by resource sentences. The algorithm has been developed and evaluated in the domain of science education where coreness refers to the degree to which a sentence embodies key concepts important to developing a robust understanding of the domain. Our method operates by automatically computing and leveraging the degree of semantic similarity between resource sentences and standard domain concepts designed by human experts for various STEM domains. In our experiments, the algorithm demonstrates high accuracy in identifying sentence coreness when there is agreement between human experts on the coreness rating. We also present performance comparisons with a number of baseline systems.

Original languageEnglish (US)
Title of host publication2014 IEEE/ACM Joint Conference on Digital Libraries, JCDL 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-388
Number of pages10
ISBN (Electronic)9781479955695
DOIs
StatePublished - Dec 1 2014
Externally publishedYes
Event2014 14th IEEE/ACM Joint Conference on Digital Libraries, JCDL 2014 - London, United Kingdom
Duration: Sep 8 2014Sep 12 2014

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996

Conference

Conference2014 14th IEEE/ACM Joint Conference on Digital Libraries, JCDL 2014
Country/TerritoryUnited Kingdom
CityLondon
Period9/8/149/12/14

Keywords

  • Core concepts
  • Semantic similarity
  • Text summarization

ASJC Scopus subject areas

  • Engineering(all)

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