Abstract
We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur. In particular, we combine a TF.IDF representation of the document with features specifically engineered for the task, and we make predictions via an ensemble of diverse classifiers including SVM, Random Forest, AdaBoost, MLP, and LightGBM. Whenever the model detects that style change is present, we apply it recursively, looking to find the specific positions of the change. Our approach powered the winning system for the PAN@CLEF 2018 task on Style Change Detection.
D. Kopev, D. Zlatkova, K. Mitov and A. Atanasov—Equal Contribution.
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In this dataset, style change also means switch of authorship.
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Acknowledgements
This work was supported by the Bulgarian National Scientific Fund within the project no. DN 12/9, and by the Scientific Fund of the Sofia University within project no. 80-10-162/25.04.2018.
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Kopev, D. et al. (2018). Recursive Style Breach Detection with Multifaceted Ensemble Learning. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_12
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