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

arXiv: https://arxiv.org/abs/2501.10072 · HTML: https://mlsanity.com/paper/2501.10072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks](https://mlsanity.com/paper/1811.01753/) (2018)
- [A statistical method for analyzing and comparing spatiotemporal cortical activation patterns](https://mlsanity.com/paper/1611.07677/) (2016)
- [Analysis of structure and dynamics in three-neuron motifs](https://mlsanity.com/paper/1811.05225/) (2018)
- [Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity](https://mlsanity.com/paper/2206.01922/) (2022)
- [Quantifying and maximizing the information flux in recurrent neural networks](https://mlsanity.com/paper/2301.12892/) (2023)

## Abstract (arXiv)

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.