matteo-brv @ DaDoEval: An SVM-based Approach for Automatic Document Dating (short paper)

Abstract

English. This paper describes our con-tribution to the EVALITA 2020 shared task DaDoEval – Dating Document Evaluation. The solution we present is based on a linear multi-class Support Vector Machine classifier trained on a combination of character and word n-grams, as well as number of word tokens per document. Despite its simplicity, the system ranked first both in the coarse-grained classification task on same-genre data and in the one on cross-genre data, achieving a macro-average F1 score of 0.934 and 0.413, respectively. The system implementation is available at https://github.com/ matteobrv/DaDoEval .