Technical Communication Quarterly Jan 2023

Building Better Machine Learning Models for Rhetorical Analyses: The Use of Rhetorical Feature Sets for Training Artificial Neural Network Models

Zoltan P. Majdik Dagestan State University ; James Wynn

Abstract

In this paper, we investigate two approaches to building artificial neural network models to compare their effectiveness for accurately classifying rhetorical structures across multiple (non-binary) classes in small textual datasets. We find that the most accurate type of model can be designed by using a custom rhetorical feature list coupled with general-language word vector representations, which outperforms models with more computing-intensive architectures.

Journal
Technical Communication Quarterly
Published
2023-01-02
DOI
10.1080/10572252.2022.2077452
Open Access
Closed
Topics
multimodality artificial intelligence

Citation Context

Cited by in this index (7)

  1. Shivers-McNair (2025)
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  5. Yang et al. (2024)
    Pathological Liars: Algorithmic Knowing in the Rhetorical Ecosystem of …
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  1. Lubin et al. (2024)
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  2. Majdik et al. (2024)
    Rhetoric of/with AI: An Introduction
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Cites in this index (11)

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    Technical Communication Quarterly
  2. Graham (2021)
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    Written Communication
  3. Omizo (2020)
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  4. Gallagher et al. (2020)
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    Technical Communication Quarterly
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    Technical Communication Quarterly
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  1. Kessler et al. (2018)
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    Technical Communication Quarterly
  2. Jones (2016)
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