A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text
This work addresses the challenge of understanding subjective style in text for applications in natural language processing, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of modeling writing style in text by proposing a multi-level analysis from lexical, syntactic, and semantic perspectives, and demonstrated its utility in improving performance on downstream tasks like authorship attribution and emotion prediction across diverse datasets.
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.