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Nonuniform language in technical writing: Detection and correction

Published online by Cambridge University Press:  06 March 2020

Weibo Wang ,
Aminul Islam ,
Abidalrahman Moh’d ,
Axel J. Soto Open the ORCID record for Axel J. Soto [Opens in a new window]  and
Evangelos E. Milios
Weibo Wang
Affiliation:
Faculty of Computer Science, Dalhousie University, Canada Dash Hudson, Canada
Aminul Islam
Affiliation:
School of Computing and Informatics, University of Louisiana at Lafayette, USA
Abidalrahman Moh’d
Affiliation:
Department of Mathematics and Computer Science, Eastern Illinois University, USA
Axel J. Soto*
Affiliation:
Institute for Computer Science and Engineering, CONICET–UNS, Argentina Department of Computer Science and Engineering, Universidad Nacional del Sur, Argentina
Evangelos E. Milios
Affiliation:
Faculty of Computer Science, Dalhousie University, Canada
*
*Corresponding author. E-mail: [email protected]
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Abstract

Technical writing in professional environments, such as user manual authoring, requires the use of uniform language. Nonuniform language refers to sentences in a technical document that are intended to have the same meaning within a similar context, but use different words or writing style. Addressing this nonuniformity problem requires the performance of two tasks. The first task, which we named nonuniform language detection (NLD), is detecting such sentences. We propose an NLD method that utilizes different similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different features are extracted and integrated by applying a machine learning classification method. The second task, which we named nonuniform language correction (NLC), is deciding which sentence among the detected ones is more appropriate for that context. To address this problem, we propose an NLC method that combines contraction removal, near-synonym choice, and text readability comparison. We tested our methods using smartphone user manuals. We finally compared our methods against state-of-the-art methods in paraphrase detection (for NLD) and against expert annotators (for both NLD and NLC). The experiments demonstrate that the proposed methods achieve performance that matches expert annotators.

Keywords

Technical languageText similarityText simplificationSemantic SimilaritySentence similarityParaphrase detectionText error detectionText error correction
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 3 , May 2021 , pp. 293 - 314
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Copyright
© Cambridge University Press 2020

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