Applicable Authorship Verification
Table 5. Evaluation results for the test corpus CPerv.
['[BOLD] AV Method', '[BOLD] c@1', '[BOLD] AUC', '[BOLD] TP', '[BOLD] FN', '[BOLD] FP', '[BOLD] TN', '[BOLD] UP']
The Effect of Limited Text Length: In our third experiment, we investigate the question how text lengths affect the results of the examined AV methods. The motivation behind this experiment is based on the observation of Stamatatos et al. that text length is an important issue, which has not been thoroughly studied within authorship verification research. The corpus is suitable for this purpose, as it comprises a large number of verification problems, where more than 90% of all documents have sufficient text lengths (≥2,000 characters). This allows a stepwise truncation and by this to analyze the effect between the text lengths and the recognition results. Although these results are quite impressive, it should be noted that a large fraction of the documents comprises thousands of words. Thus, the methods can learn precise representations based on a large variety of features, which in turn enable a good determination of (dis)similarities between known/unknown documents. To investigate this issue in more detail, we constructed four versions of the test corpus and equalized the unknown document lengths to 250, 500, 1000, and 2000 characters. Then, we applied the top performing AV methods with a c@1 value >0.9 on the four corpora. Here, we reused the same models and hyperparameters (including the decision criteria θ and θM) that were determined on the training corpus. The intention behind this was to observe the robustness of the trained AV models, given the fact that during training they were confronted with longer documents.