Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions

Krauß P, Schilling A (2025)

Publication Type: Conference contribution

Publication year: 2025

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: Proceedings of the International Joint Conference on Neural Networks

Event location: Rome IT

DOI: 10.1109/IJCNN64981.2025.11228988

Abstract

Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions using part-of-speech (POS) tagging and bigram analysis. By leveraging deep neural networks, we classify literary authors based on POS tag vectors and bigram frequency matrices derived from their works. We employ fully connected and convolutional neural network architectures to explore the efficacy of unigram and bigram-based representations. Our results demonstrate that while unigram features achieve moderate classification accuracy, bigram-based models significantly improve performance, suggesting that sequential word class patterns are more distinctive of authorial style. Multi-dimensional scaling (MDS) visualizations reveal meaningful clustering of authors' works, supporting the hypothesis that stylistic nuances can be captured through computational methods. These findings highlight the potential of deep learning and linguistic feature analysis for author profiling and literary studies.

Authors with CRIS profile

Patrick Krauß Lehrstuhl für Informatik 5 (Mustererkennung) Achim Schilling Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik)

How to cite

APA:

Krauß, P., & Schilling, A. (2025). Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions. In Proceedings of the International Joint Conference on Neural Networks. Rome, IT: Institute of Electrical and Electronics Engineers Inc..

MLA:

Krauß, Patrick, and Achim Schilling. "Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions." Proceedings of the 2025 International Joint Conference on Neural Networks, IJCNN 2025, Rome Institute of Electrical and Electronics Engineers Inc., 2025.

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