Recursive Style Breach Detection with Multifaceted Ensemble Learning
This work addresses style change detection for text analysis, but it is incremental as it builds on existing methods with an ensemble approach.
The authors tackled style change detection in text documents by combining TF.IDF representations with engineered features and using an ensemble of diverse classifiers, achieving first place in the PAN@CLEF 2018 competition.
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.