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
收藏
页码：201 / 212
页数：12
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