Proximity based one-class classification with Common N-Gram dissimilarity for authorship verification task Notebook for PAN at CLEF 2013
Bibliographic record
Abstract
We describe our participation in the Author Identification task of the PAN 2013 competition. This competition task presents participants with a set of authorship verification problems. In each such a problem, one is given a set of documents written by one author and a sample document; the task is to answer the question whether or not the sample document was written by the same author as the remaining documents. We approach this problem by proposing a proximity based method for one-class classification (based on an idea similar to the k -center boundary method) that applies the Common N-Gram (CNG) dissimilarity mea- sure. The CNG dissimilarity is based on the differences in the frequencies of the character n-grams that are most common in the considered documents. Our method compares the dissimilarity between the sample document and each doc- ument from the target set of documents of known authorship to the maximum dissimilarity between this target document and all other documents from the set; thresholding is applied to arrive at the classification of the sample documen t. Our method yielded F1 of 0.659 on the whole competition test dataset and the com- petition ranking 5th (shared) of 18 (according to the results announced on June 12, 2013).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".