An improved N-grams based Model for Authorship Attribution

被引：1

作者：

Boughaci, Dalila ^{[1
]}

Benmesbah, Mounir ^{[1
]}

Zebiri, Aniss ^{[1
]}

机构：

[1] Univ Sci & Technol Houari Boumediene, Dept Comp Sci, BP 32 El Alia, Algiers 16111, Algeria

来源：

2019 INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCIS) | 2019年

关键词：

Authorship attribution; N-gram; similarity functions; Euclidian distance; text classification; IDENTIFICATION;

D O I：

10.1109/iccisci.2019.8716391

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Authorship attribution is the problem of studying an anonymous text and finding the corresponding author in a set of candidate authors. In this paper, we propose a method based on N-grams model for the problem of authorship attribution. Several measures are used to assign an anonymous text to an author. The different variants of the proposed method are implemented and validated on PAN benchmarks. The numerical results are encouraging and demonstrate the benefit of the proposed idea.

引用

页码：70 / 75

页数：6

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