An Ensemble Approach to Cross-Domain Authorship Attribution

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Custodio, Jose Eleandro ; Paraboni, Ivandre ; Crestani, F ; Braschler, M ; Savoy, J ; Rauber, A ; Muller, H ; Losada, DE ; Burki, GH ; Cappellato, L ; Ferro, N

• Tipo de documento: Artigo Científico
• Fonte: EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2019); v. 11696, p. 12-pg., 2019-01-01.

Resumo

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. (AU)

• Processo FAPESP: 16/14223-0 - Tratamento Computacional da Personalidade Humana para Aplicações de Processamento de Língua Natural
• Beneficiário: Ivandre Paraboni
• Modalidade de apoio: Auxílio à Pesquisa - Regular