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

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          Abstract

          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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          Most cited references16

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          CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON

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            The Proposition Bank: An Annotated Corpus of Semantic Roles

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              Affect analysis of text using fuzzy semantic typing

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                Author and article information

                Journal
                18 September 2019
                Article
                1909.08349
                2c7fb468-0e7f-48c0-9a72-732c3b14dc3b

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.CL cs.LG

                Theoretical computer science,Artificial intelligence
                Theoretical computer science, Artificial intelligence

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