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

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arxiv 1909.08349 v1 pith:UXKYTCJV submitted 2019-09-18 cs.CL cs.LG

classification cs.CLcs.LG
keywords styletasksunderstandinganalysisdatasetsdownstreamelementslanguage
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models

    cs.CL 2025-01 reject novelty 6.0 of 10

    On a Queneau-Fénéon corpus, topic variation increases embedding dispersion more than style variation, but the style effect is confounded with human versus GPT-4o authorship.

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