| Resumen |
We compare the performance of character n-gram features (n = 3−8) and lexical features (unigrams and bigrams of words), as well as their combinations, on the tasks of authorship attribution, author profiling, and discriminating between similar languages. We developed a single multi-labeled corpus for the three aforementioned tasks, composed of news articles in different varieties of Spanish.We used the same machinelearning algorithm, Liblinear SVM, in order to find out which features are more predictive and for which task. Our experiments show that higherorder character n-grams (n = 5−8) outperform lower-order character n-grams, and the combination of all word and character n-grams of different orders (n = 1−2 for words and n = 3−8 for characters) usually outperforms smaller subsets of such features. We also evaluate the performance
of character n-grams, lexical features, and their combinations when reducing all named entities to a single symbol “NE” to avoid topicdependent features. |
