4 research outputs found
Similarity Learning for Authorship Verification in Social Media
Authorship verification tries to answer the question if two documents with
unknown authors were written by the same author or not. A range of successful
technical approaches has been proposed for this task, many of which are based
on traditional linguistic features such as n-grams. These algorithms achieve
good results for certain types of written documents like books and novels.
Forensic authorship verification for social media, however, is a much more
challenging task since messages tend to be relatively short, with a large
variety of different genres and topics. At this point, traditional methods
based on features like n-grams have had limited success. In this work, we
propose a new neural network topology for similarity learning that
significantly improves the performance on the author verification task with
such challenging data sets.Comment: 5 pages, 3 figures, 1 table, presented on ICASSP 2019 in Brighton, U
Classification of authors for an automatic recommendation process for criminal responsibility
One problem in classifying tasks is the handling of features that characterize classes. When the list of features is long, a noise resistant algorithm of irrelevant features can be used, or these features can be reduced. Authorship attribution is a task that assigns an anonymous text to a subject on a list of possible authors, has been widely addressed as an automatic text classification task. In it, n-grams can produce long lists of features even in small corpora. Despite this, there is a lack of research exposing the effects of using noise-resistant algorithms, reducing traits, or combining both options. This paper responds to this lack by using contributions to discussion forums related to organized crime. The results show that the classifiers evaluated, in general, benefit from feature reduction, and that, thanks to such reduction, even classical algorithms outperform state-of-the-art classifiers considered highly noise resistant