A language-independent authorship attribution approach for author identification of text documents

作者：

Highlights：

• A new lazy classifier for the authorship attribution task.

• A new similarity metric to calculate the similarity between documents.

• A language-independent classifier without need to any NLP techniques.

• Examining the effects of different classifiers and stylometric features on the authorship attribution accuracy.

摘要

•A new lazy classifier for the authorship attribution task.•A new similarity metric to calculate the similarity between documents.•A language-independent classifier without need to any NLP techniques.•Examining the effects of different classifiers and stylometric features on the authorship attribution accuracy.

论文关键词：Authorship attribution,Author identification,Text similarity,Term frequency,Inverse document frequency,NLP

论文评审过程：Received 6 May 2020, Revised 9 April 2021, Accepted 28 April 2021, Available online 4 May 2021, Version of Record 30 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115139