A spreading activation framework for tracking conceptual complexity of texts

Hulpus, Ioana ; Štajner, Sanja ; Stuckenschmidt, Heiner

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URL: https://madoc.bib.uni-mannheim.de/51558
Additional URL: https://www.aclweb.org/anthology/P19-1377
URN: urn:nbn:de:bsz:180-madoc-515580
Document Type: Conference or workshop publication
Year of publication: 2019
Book title: 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 : Proceedings of the conference : July 28 - August 2, 2019, Florence, Italy
Page range: 3878-3887
Conference title: 57th Annual Meeting of the Association for Computational Linguistics
Location of the conference venue: Florence, Italy
Date of the conference: 28 July 2019-2nd August 2019
Publisher: Korhonen, Anna
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics, ACL
ISBN: 978-1-950737-48-2
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.

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