Abstract
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.
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Acknowledgements
The second author received financial support from FAPESP grant nro. 2016/14223-0 and by the University of São Paulo. The authors also thank the PAN-CLEF AA shared task organisers, and the anonymous reviewers for their valuable input.
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Custódio, J.E., Paraboni, I. (2019). An Ensemble Approach to Cross-Domain Authorship Attribution. In: Crestani, F., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science(), vol 11696. Springer, Cham. https://doi.org/10.1007/978-3-030-28577-7_17
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