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