An Ensemble Approach to Cross-Domain Authorship Attribution

被引：7

作者：

Custodio, Jose Eleandro ^{[1
]}

Paraboni, Ivandre ^{[1
]}

机构：

[1] Univ Sao Paulo, Sch Arts Sci & Humanities EACH, Sao Paulo, Brazil

来源：

EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2019) | 2019年 / 11696卷

基金：

巴西圣保罗研究基金会;

关键词：

D O I：

10.1007/978-3-030-28577-7_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an ensemble approach to cross-domain authorship attribution that combines predictions made by three independent classifiers, namely, standard character n-grams, character n-grams with non-diacritic distortion and word n-grams. Our proposal relies on variable-length n-gram models and multinomial logistic regression to select the prediction of highest probability among the three models as the output for the task. The present approach is compared against a number of baseline systems, and we report results based on both the PAN-CLEF 2018 test data, and on a new corpus of song lyrics in English and Portuguese.

引用

页码：201 / 212

页数：12

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