When shallow is good enough: Automatic assessment of conceptual text complexity using shallow semantic features

Štajner, Sanja ; Hulpus, Ioana

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URL: https://madoc.bib.uni-mannheim.de/55729
Additional URL: https://www.aclweb.org/anthology/2020.lrec-1.177/
URN: urn:nbn:de:bsz:180-madoc-557291
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: LREC 2020 Marseille : Twelfth International Conference on Language Resources and Evaluation : May 11-16, 2020, Palais du Pharo, Marseille, France : conference proceedings
Page range: 1414-1422
Conference title: LREC 2020 (conference canceled)
Location of the conference venue: Marseille, France
Date of the conference: canceled
Publisher: Calzolari, Nicoletta
Place of publication: Paris
Publishing house: European Language Resources Association, ELRA-ELDA
ISBN: 979-10-95546-34-4
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Pre-existing license: Creative Commons Attribution, Non-Commercial 4.0 International (CC BY-NC 4.0)
Subject: 020 Library and information sciences
Abstract: According to psycholinguistic studies, the complexity of concepts used in a text and the relations between mentioned concepts play the most important role in text understanding and maintaining reader’s interest. However, the classical approaches to automatic assessment of text complexity, and their commercial applications, take into consideration mainly syntactic and lexical complexity. Recently, we introduced the task of automatic assessment of conceptual text complexity, proposing a set of graph-based deep semantic features using DBpedia as a proxy to human knowledge. Given that such graphs can be noisy, incomplete, and computationally expensive to deal with, in this paper, we propose the use of textual features and shallow semantic features that only require entity linking. We compare the results obtained with new features with those of the state-of-the-art deep semantic features on two tasks: (1) pairwise comparison of two versions of the same text; and (2) five-level classification of texts. We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task. Interestingly, the combination of the shallow and deep semantic features lead to a significant improvement of the performances on that task.

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