Authorship Verification - An Approach based on Random Forest
This addresses authorship attribution for applications in information retrieval, computational linguistics, law, and journalism, but appears incremental.
The paper tackled authorship verification for cross-genre and cross-topic tasks using word-based and style-based features with a Random Forest classifier, achieving results in the PAN at CLEF 2015 competition.
Authorship attribution, being an important problem in many areas in-cluding information retrieval, computational linguistics, law and journalism etc., has been identified as a subject of increasingly research interest in the re-cent years. In case of Author Identification task in PAN at CLEF 2015, the main focus was given on cross-genre and cross-topic author verification tasks. We have used several word-based and style-based features to identify the dif-ferences between the known and unknown problems of one given set and label the unknown ones accordingly using a Random Forest based classifier.