# A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text

arXiv: https://arxiv.org/abs/1909.08349 · HTML: https://mlsanity.com/paper/1909.08349/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://mlsanity.com/paper/2503.02450/) (2025)
- [Style Control for Schema-Guided Natural Language Generation](https://mlsanity.com/paper/2109.12211/) (2021)
- [Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning](https://mlsanity.com/paper/2409.04574/) (2024)
- [DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting](https://mlsanity.com/paper/2101.11836/) (2021)
- [Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding](https://mlsanity.com/paper/2305.14592/) (2023)
- [Incorporating Stylistic Lexical Preferences in Generative Language Models](https://mlsanity.com/paper/2010.11553/) (2020)
- [Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection](https://mlsanity.com/paper/2401.15509/) (2024)
- [Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models](https://mlsanity.com/paper/2501.00828/) (2025)
- [Measuring Embedding Sensitivity to Authorial Style in French: Comparing Literary Texts with Language Model Rewritings](https://mlsanity.com/paper/2605.10606/) (2026)

## Abstract (arXiv)

With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic. We discuss the stylistically expressive elements within each of these levels and use existing methods to quantify the linguistic intuitions related to some of these elements. We show that such a multi-level analysis is useful for developing a well-knit understanding of style - which is independent of the natural language task at hand, and also demonstrate its value in solving three downstream tasks: authors' style analysis, authorship attribution, and emotion prediction. We conduct experiments on a variety of datasets, comprising texts from social networking sites, user reviews, legal documents, literary books, and newswire. The results on the aforementioned tasks and datasets illustrate that such a multi-level understanding of style, which has been largely ignored in recent works, models style-related subjectivity in text and can be leveraged to improve performance on multiple downstream tasks both qualitatively and quantitatively.