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+# Authorship Verification for Medieval Latin 
+
+Code to reproduce the experiments reported in the papers
+["The Epistle to Cangrande Through the Lens of Computational Authorship Verification"](https://link.springer.com/chapter/10.1007/978-3-030-30754-7_15)
+and 
+["L’Epistola a Cangrande al vaglio della Computational Authorship Verification: Risultati preliminari (con una postilla sulla cosiddetta XIV Epistola di Dante Alighieri)"](https://www.academia.edu/42297516/L_Epistola_a_Cangrande_al_vaglio_della_Computational_Authorship_Verification_risultati_preliminari_con_una_postilla_sulla_cosiddetta_XIV_Epistola_di_Dante_Alighieri_in_Nuove_inchieste_sull_Epistola_a_Cangrande_a_c._di_A._Casadei_Pisa_Pisa_University_Press_pp._153-192)
+
+## Disclaimer:
+The dataset is not distributed in this version. We have asked the Editors for permission to publish the corpus.
+We are waiting for some of these responses to arrive.
+
+## Running the Experiments
+The script in __./src/author_identification.py__ executes the experiments. This is the script syntax (--help):
+
+```
+Authorship verification for Epistola XIII
+
+positional arguments:
+  PATH               Path to the directory containing the corpus (documents
+                     must be named <author>_<texname>.txt)
+  positive           Positive author for the hypothesis (default "Dante"); set
+                     to "ALL" to check every author
+
+optional arguments:
+  -h, --help         show this help message and exit
+  --loo              submit each binary classifier to leave-one-out validation
+  --unknown PATH     path to the file of unknown paternity (default None)
+```
+
+The following command line:
+```
+cd src
+python author_identification.py ../Corpora/CorpusI Dante --unknown ../Epistle/EpistolaXIII_1.txt
+```
+
+Will use all texts in ../Corpora/CorpusI as training documents to train a verificator for the 
+file ../Epistle/EpistolaXIII_1.txt assuming Dante is the positive class (i.e., it will check if, on the
+basis of the evidence shown in other Dante's texts, the unknown one belongs to Dante or not). 
+The output is probabilistic, informing of the uncertainty that the classifier has in attributing the document
+to the positive class. 
+
+Similarly, the command line:
+```
+cd src
+python author_identification.py ../Corpora/CorpusI Dante --loo 
+```
+will perform a cross-validation of the binary classifier for Dante using all training documents in a leave-one-out (LOO) fashion.