Comparison of Character n-grams and Lexical Features on Author, Gender, and Language Variety Identification on the Same Spanish News Corpus

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 machine-learning algorithm, Liblinear SVM, in order to find out which features are more predictive and for which task. Our experiments show that higher-order 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 topic-dependent features.