167 research outputs found
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
No full textAuthorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author (A) or by someone else ( A ̄ ̄ ̄ ̄ ). Itehas been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, oreby imitating the style of another author. Inethis paper, weeinvestigate the potential benefits of augmenting the classifier training set with (negative) synthetic examples. These synthetic examples are generated to imitate the style of A. Weeanalyze the improvements in the classifier predictions that this augmentation brings to bear in the task of AV in an adversarial setting. Ineparticular, weeexperiment with three different generator architectures (one based on Recurrent Neural Networks, another based on small-scale transformers, and another based on the popular GPT model) and with two training strategies (one inspired by standard Language Models, and another inspired by Wasserstein Generative Adversarial Networks). Weeevaluate our hypothesis on five datasets (three of which have been specifically collected to represent an adversarial setting) and using two learning algorithms for the AV classifier (Support Vector Machines and Convolutional Neural Networks). This experimentation yields negative results, revealing that, although our methodology proves effective in many adversarial settings, its benefits are too sporadic for a pragmatical application
Ravenna bizantina
No full textContestualizzazione dei reperti di età bizantina conservati nel Museo di Class
Syllabic quantity patterns as rhythmic features for Latin authorship attribution
No full textIt is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so-called syllabic quantity, that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic-agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors
Ravenna e il mare
No full textIn questo contributo sono presentati alcuni reperti-chiave della sezione del nuovo allestimento museale di Classis, in particolar modo il reparto relativo ai rapporti di Ravenna con il mare, dalla tarda età repubblicana all'alto Medioev
: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts
No full textWe present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research
on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively,
labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and
treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis
tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets, we provide
experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a
text of unknown authorship was written by a candidate author. We also make available the source code of the authorship
verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other
researchers. We also describe the application of the above authorship verification system, using these datasets as training
data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars.
on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively,
labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and
treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis
tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets, we provide
experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a
text of unknown authorship was written by a candidate author. We also make available the source code of the authorship
verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other
researchers. We also describe the application of the above authorship verification system, using these datasets as training
data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars
Explainable Authorship Identification in Cultural Heritage Applications
No full textWhile a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this article, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification and same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards XAI, more work remains to be done to provide tools that can be profitably integrated into the workflows of scholars
The Epistle to Cangrande Through the Lens of Computational Authorship Verification
No full textThe Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open
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