Abstract
The 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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Notes
- 1.
Other works by Alighieri, including the Divine Comedy, are not included in these two datasets since Alighieri wrote them not in Latin but in the Florentine vernacular (volgare), which was to form the basis of what is nowadays the Italian language.
- 2.
In the medieval writing there was only one grapheme, represented as a lowercase “u” and a capital “V”, instead of the two modern graphemes “u-U” and “v-V”.
- 3.
- 4.
“The” random classifier is indeed an abstraction; by the accuracy of the random classifier we mean the average accuracy of all possible classifiers, i.e., of all possible ways the test set might be classified. It is easy to show that this is equivalent to the accuracy of a classifier for which half of the positives are true positives while the other half are false negatives, and half of the negatives are true negatives while the other half are false positives.
- 5.
Note that this specific problem is, as stated, confined to the classifier of EpXIII(II), and does not affect the one for EpXIII(I). Still, in the EpXIII(I) dataset there might be other types of topic bias that we have not detected yet.
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Corbara, S., Moreo, A., Sebastiani, F., Tavoni, M. (2019). The Epistle to Cangrande Through the Lens of Computational Authorship Verification. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_15
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