Automatic assessment of conceptual text complexity using knowledge graphs

Štajner, Sanja ; Hulpus, Ioana

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URN: urn:nbn:de:bsz:180-madoc-515598
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: 27th International Conference on Computational Linguistics, COLING 2018 : Proceedings of the conference : August 20-26, 2018, Santa Fe, New Mexico, USA
Page range: 318-330
Conference title: 27th International Conference on Computational Linguistics
Location of the conference venue: Santa Fe, NM
Date of the conference: 20-26 August 2018
Publisher: Gurevych, Iryna
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics, ACL
ISBN: 978-1-948087-50-6
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.

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