Style Features for Authors in Two Languages

Stuart, Lauren M.; Tazhibayeva, Saltanat; Wagoner, Amy R.; Taylor, Julia M.

doi:10.1109/wi-iat.2013.65

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…A comparative analysis was performed between the proposed and character Bi-gram methods using 200 articles by five well-known modern writers. In addition, Stuart et al [17] proposed measuring certain expressible writing style features. Its uses include author characterization for recognition in text whose authorship is disputed or unknown.…”

Section: Stylistic Modelsmentioning

confidence: 99%

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

He,

Lashkari,

Vombatkere

et al. 2024

Information

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Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.

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Section: Stylistic Modelsmentioning

confidence: 99%

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

He,

Lashkari,

Vombatkere

et al. 2024

Information

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show abstract

“…Moreover, in existing studies based on cross-lingual authorship identificaton [4,28,47,46], the number of candidate authors does not exceed 8. Our objective is to design a solution that can support hundreds of candidate authors for the given document.…”

Section: Limitationmentioning

confidence: 99%

“…A class of existing methods rely on a certain form of internal language knowledge, e.g., a machine transla-tor [4,28,46,47]. These methods have reported a high accuracy for resourceful languages using a high dimensional feature set (i.e., word-based n-grams) [4,28,47]. However, they have the following limitations.…”

Section: Introductionmentioning

confidence: 99%

A scalable framework for cross-lingual authorship identification

Sarwar

Rakthanmanon

et al. 2018

Information Sciences

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“…Preliminary results suggest that the resulting feature set distinguished between authors and provided some power in distinguishing between languages. Full details and discussion of the results can be found in Stuart et al (2013).…”

Section: Work On Interlanguage Featuresmentioning

confidence: 99%

On Identifying Authors with Style

Stuart

Tazhibayeva

Wagoner

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Stylometry is the quantified (often statistical) analysis of author style as a set of (usually morphosyntactic) features expressed in several documents by the author. The focus of this paper is a task to which stylometry is often applied: authorship attribution, the question of identifying or confirming the author of a text based on the known body of work. We analyze a feature set previously introduced in the field, using a tool and corpus already available. Decomposing the set, we identify the features that seem to have contributed the most to accurate performance. In re-composing the set under different objectives -first, for English-only document sets, and then for possible multi-language use -we identify smaller sets of feature combinations that work well together in accurate performance. We then outline our continuing work based on the results we obtain.

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Style Features for Authors in Two Languages

Cited by 7 publications

References 8 publications

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

A scalable framework for cross-lingual authorship identification

On Identifying Authors with Style

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