Automatic Compositor Attribution in the First Folio of Shakespeare
Table 1: The experimental results for all setups of the model. In the experiments with basic model, we compare the short hinman word list with the automatically filtered auto word list. We show results for several variants of our full model, labeled as feat, both with and without spacing generation. A random baseline is included for comparison.
['Model Setup', 'Model Setup', 'Bodleian Transcription Hinman Attr', 'Bodleian Transcription Hinman Attr', 'Bodleian Transcription Blayney Attr', 'Bodleian Transcription Blayney Attr', 'Ocular OCR Transcription Hinman Attr', 'Ocular OCR Transcription Hinman Attr', 'Ocular OCR Transcription Blayney Attr', 'Ocular OCR Transcription Blayney Attr']
The basic variant, modeled after Hinman’s original procedure, substantially outperforms the random baseline, with the hinman word list outperforming the larger auto word list. However, use of the larger word list with feature-based models yields large gains in all scenarios, including evaluation on Hinman’s original attributions and while using OCR diplomatic transcriptions. The best-performing model for both manually transcribed and OCR text uses edit features in conjunction with spacing generation and achieves an accuracy of up to 87%. Including word features on top of this leads to slightly reduced performance, perhaps as a result of the substantially increased number of free parameters. In the OCR scenario, the addition of word features on top of edit decreases accuracy, unlike the same experiment with the manual transcription. This is possibly a result of the reduced reliability of full word forms due to mistakes in OCR.