A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2019.
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Citing papers per year: 2020: 1; 2021: 2; 2022: 0; 2023: 1; 2024: 2; 2025: 2.
8 papers.
- 2025Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalizationcited by 44
- 2021Style Control for Schema-Guided Natural Language Generationcited by 10
- 2024Customizing Large Language Model Generation Style using Parameter-Efficient Finetuningcited by 7
- 2021DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewritingcited by 5
- 2023Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understandingcited by 4
- 2020Incorporating Stylistic Lexical Preferences in Generative Language Modelscited by 2
- 2024Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detectioncited by 2
- 2025Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Modelscited by 1
- 2025Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization4 Mar 2025
- 2025Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models1 Jan 2025
- 2024Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning6 Sep 2024
- 2024Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection27 Jan 2024
- 2023Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding24 May 2023
- 2021Style Control for Schema-Guided Natural Language Generation24 Sep 2021
- 2021DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting28 Jan 2021
- 2020Incorporating Stylistic Lexical Preferences in Generative Language Models22 Oct 2020