| Full text | |
| Author(s): Show less - |
Custodio, Jose Eleandro
;
Paraboni, Ivandre
;
Crestani, F
;
Braschler, M
;
Savoy, J
;
Rauber, A
;
Muller, H
;
Losada, DE
;
Burki, GH
;
Cappellato, L
;
Ferro, N
Total Authors: 11
|
| Document type: | Journal article |
| Source: | EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2019); v. 11696, p. 12-pg., 2019-01-01. |
| Abstract | |
This paper presents an ensemble approach to cross-domain authorship attribution that combines predictions made by three independent classifiers, namely, standard character n-grams, character n-grams with non-diacritic distortion and word n-grams. Our proposal relies on variable-length n-gram models and multinomial logistic regression to select the prediction of highest probability among the three models as the output for the task. The present approach is compared against a number of baseline systems, and we report results based on both the PAN-CLEF 2018 test data, and on a new corpus of song lyrics in English and Portuguese. (AU) | |
| FAPESP's process: | 16/14223-0 - Computational Treatment of Human Personality for Natural Language Processing Applications |
| Grantee: | Ivandre Paraboni |
| Support Opportunities: | Regular Research Grants |