Popescu, MariusMariusPopescuGrozea, CristianCristianGrozea2022-03-122022-03-122012https://publica.fraunhofer.de/handle/publica/379107This paper presents our approach to the PAN 2012 Traditional Authorship Attribution tasks and the Sexual Predator Identification task. We approached these tasks with machine learning methods that work at the character level. More precisely, we treated texts as just sequences of symbols (strings) and used string kernels in conjunction with different kernel-based learning methods: supervised and unsupervised. The results were extremely good, we ranked first in most problem and overall in the traditional authorship attribution task, according to the evaluation provided by the organizers.enauthorship analysisnatural language processingstring kernelskernel methodsmachine learning004conference paper