BiGBERT: Classifying Educational Web Resources for Kindergarten-12th Grades

Garrett Allen*, Brody Downs, Aprajita Shukla, Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, Maria Soledad Pera

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


In this paper, we present BiGBERT, a deep learning model that simultaneously examines URLs and snippets from web resources to determine their alignment with children’s educational standards. Preliminary results inferred from ablation studies and comparison with baselines and state-of-the-art counterparts, reveal that leveraging domain knowledge to learn domain-aligned contextual nuances from limited input data leads to improved identification of educational web resources.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Proceedings
EditorsDjoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani
Number of pages9
ISBN (Print)9783030722395
Publication statusPublished - 2021
Externally publishedYes
Event43rd European Conference on Information Retrieval, ECIR 2021 - Virtual, Online
Duration: 28 Mar 20211 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd European Conference on Information Retrieval, ECIR 2021
CityVirtual, Online


  • BERT
  • Educational standards
  • Web classification


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