Authorship Verification of the Disputed Pauline Letters through Deep Learning
In the Christian tradition, fourteen letters of the New Testament have been attributed to the Apostle Paul. However, for seven of these letters—1 and 2 Timothy, Titus, Ephesians, Colossians, 2 Thessalonians, and Hebrews —the attribution to Paul has been the subject of scholarly debate. This study ai...
| Auteurs: | ; |
|---|---|
| Type de support: | Électronique Article |
| Langue: | Anglais |
| Vérifier la disponibilité: | HBZ Gateway |
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Publié: |
2025
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| Dans: |
HIPHIL Novum
Année: 2025, Volume: 10, Numéro: 1, Pages: 22-39 |
| Sujets non-standardisés: | B
Apprentissage profond
B Text Classification B Pauline Epistles B Authorship Attribution |
| Accès en ligne: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| 520 | |a In the Christian tradition, fourteen letters of the New Testament have been attributed to the Apostle Paul. However, for seven of these letters—1 and 2 Timothy, Titus, Ephesians, Colossians, 2 Thessalonians, and Hebrews —the attribution to Paul has been the subject of scholarly debate. This study aims to develop a bidirectional long short-term memory (BiLSTM) network to classify chunks of these disputed letters, each approximately 100 words long, as either authored by Paul or not. Two model variants—a plaintext variant and a lemmatized text variant—were trained on undisputed Pauline letters and ‘impostor letters’ which serve as negative examples of Paul’s writing. The plaintext variant achieved 84% accuracy and the lemmatized text variant 83% accuracy. Both variants classify the majority of text chunks from Colossians and 2 Thessalonians as Pauline and the majority of chunks from Hebrews and 1 Timothy as non-Pauline, although caution is warranted in drawing strong conclusions from these results. For the remaining disputed Pauline letters—Titus, 2 Timothy, and Ephesians—the majority classification varies between the model variants, further emphasizing the need for caution. Nevertheless, this study introduces a deep learning approach to the authorship verification problem of the disputed Pauline letters, potentially serving as a model for future research. | ||
| 601 | |a Deep Learning | ||
| 650 | 4 | |a Text Classification | |
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| 650 | 4 | |a Pauline Epistles | |
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