Automatic assessment of absolute sentence complexity

Štajner, Sanja ; Ponzetto, Simone Paolo ; Stuckenschmidt, Heiner

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Document Type: Conference or workshop publication
Year of publication: 2017
Book title: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) : Melbourne, Australia 19-25 August 2017
Page range: 4096-4102
Conference title: The 26th International Joint Conference on Artificial Intelligence (IJCAI)
Location of the conference venue: Melbourne, Australia
Date of the conference: 19-25 August 2017
Publisher: Sierra, Carles
Place of publication: Melbourne
Publishing house: International Joint Conferences on Artificial Intelligence
ISBN: 978-0-9992411-0-3
Publication language: English
Institution: Außerfakultäre Einrichtungen > SFB 884
School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): natural language processing , computational linguistics , absolute sentence complexity , sentence classification
Abstract: Lexically and syntactically simpler sentences result in shorter reading time and better understanding in many people. However, no reliable systems for automatic assessment of sentence complexity have been proposed so far. Instead, the assessment is usually done manually, requiring expert human annotators. To address this problem, we first define the sentence complexity assessment as a five-level classification task, and build a ‘gold standard’ dataset. Next, we propose robust systems for sentence complexity assessment, using a novel set of features based on leveraging lexical properties of freely available corpora, and investigate the impact of the feature type and corpus size on the classification performance.
Additional information: Online-Ressource

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