Using Alliteration in Authorship Attribution of Historical Texts

Ivanov, Lubomir

doi:10.1007/978-3-319-45510-5_28

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent examples of such novel stylistic features include prosodic features such as lexical stress, assonance, consonance, and alliteration. They have been shown to raise the accuracy of the attribution task when used in combination with traditional stylistic features (Ivanov 2019;Ivanov, Aebig, and Meerman 2018, Ivanov 2016, Ivanov and Petrovic 2015. Our work seeks to enhance this set of alternative stylistic features by exploring the usefulness of abstractness/concreteness of words and phrases as stylistic features for authorship attribution.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution

Ivanov

2023

FLAIRS

View full text Add to dashboard Cite

We present some early results from a research project aimed at exploring the usefulness of abstractness/concreteness as stylistic features for authorship attribution. We conjecture that authors use abstract/concrete words and phrases in suf- ficiently unique ways, so that machine learning classifiers can learn to distinguish the individual authors’ writing styles. Our approach is based on using the abstractness rat- ings of words and phrases from texts with established au- thorship to generate training vectors for different machine learning classifiers. The combined word/phrase ratings are extracted from two separate abstractness dictionaries – an approach that yields stronger results than using single ab- stractness dictionaries. The paper describes the details of our methodology and compares the results to those obtained using traditional authorship attribution stylistic features. The limitations of our current methodology and directions for further research are outlined at the end of the paper.

show abstract

Section: Introductionmentioning

confidence: 99%