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PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection

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Bibliographic Details
Published in:Transactions of the Association for Computational Linguistics
Format: Online Article RSS Article
Published: 2026
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container_title Transactions of the Association for Computational Linguistics
description
discipline_display Arts & Humanities
discipline_facet Arts & Humanities
format Online Article
RSS Article
genre Journal Article
id rss_article:25537
institution FRELIP
journal_source_facet Transactions of the Association for Computational Linguistics
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
— — — — — Linguistics and Philology
Language & Literature
Arts & Humanities
sub_discipline_display Language & Literature
sub_discipline_facet Language & Literature
subject_display — — — — — Linguistics and Philology
Language & Literature
Arts & Humanities
— — — — — Linguistics and Philology
Language & Literature
Arts & Humanities
subject_facet — — — — — Linguistics and Philology
Language & Literature
Arts & Humanities
title PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_auth PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_full PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_fullStr PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_full_unstemmed PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_short PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
title_sort pikgl: leveraging pruned knowledge graphs for explainable stance detection
topic — — — — — Linguistics and Philology
Language & Literature
Arts & Humanities
url https://direct.mit.edu/tacl/article/doi/10.1162/TACL.a.612/135721/PiKGL-Leveraging-Pruned-Knowledge-Graphs-for