CNG Text Classification for Authorship Profiling Task Notebook for PAN at CLEF 2013.
Bibliographic record
Abstract
We describe our participation in the Author Profiling task of the PAN 2013 competition. The task objective is to determine the age and the gender of an author of a document. We applied the Common N-Gram (CNG) classifier (Keselj et al., 2003) to this task. The CNG classifier uses a dissimilarity measure based on the differences in the frequencies of the character n-grams that are most common in the considered documents. To train the classifier, a class is represented by one class document created by concatenating the training documents belonging to the class. A sample document is labelled by the class with the minimum dissimilarity. For the six class classification (combinations of two possible gender labels and three possible age labels) we achieved the accuracy of 0.2814 on the English test dataset and 0.2592 on the Spanish test dataset. Our results are below the medians of the results of the competition participants.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.059 |
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".