| Título |
Improving cross-topic authorship attribution: The role of pre-processing |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 |
| Resumen |
The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions. © Springer Nature Switzerland AG 2018. |
| Observaciones |
DOI 10.1007/978-3-319-77116-8_21
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10762 |
| Lugar |
Budapest |
| País |
Hungria |
| No. de páginas |
289-302 |
| Vol. / Cap. |
10762 LNCS |
| Inicio |
2017-04-17 |
| Fin |
2017-04-23 |
| ISBN/ISSN |
9783319771151 |
