Modern Stylometry: A Review & Experimentation with Machine Learning

Autor:	Connagh Muldoon, Ahsan Ikram, Qublai Ali Khan Mirza
Rok vydání:	2021
Předmět:	business.industry Computer science Machine learning computer.software_genre Field (computer science) QA76 Authorship attribution Stylometry Literary criticism Artificial intelligence Baseline (configuration management) Construct (philosophy) business Internet of Things computer
Zdroj:	FiCloud
DOI:	10.1109/ficloud49777.2021.00049
Popis:	The problem of authorship attribution has applications from literary studies (such as the great Shakespeare/Marlowe debates) to counter-intelligence. The field of stylometry aims to offer quantitative results for authorship attribution. In this paper, we present a combination of stylometric techniques using machine learning. An implementation of the system is used to analyse chat logs and attempts to construct a stylometric model for users within the presented chat system. This allows for the authorship attribution of other works they may write under different names or within different communication systems. This implementation demonstrates accuracy of up to 84 % across the dataset, a full 34 % increase against a random-choice control baseline.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4a9294d42dfa2fe5e4bb1473b39e112 https://doi.org/10.1109/ficloud49777.2021.00049 Zobrazit plný text záznamu