Authorship Verification with Entity Coherence and Other Rich Linguistic Features Notebook for PAN at CLEF 2013.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We adopt Koppel et al.’s unmasking approach [5] as the major frame-work of our authorship verification system. We enrich Koppel et al.’s original word frequency features with a novel set of coherence features, derived from our earlier work [2], together with a full set of stylometric features. For texts written in languages other than English, some stylometric features are unavailable due to the lack of appropriate NLP tools, and their coherence features are derived from their translations produced by Google Translate service. Evaluated on the training corpus, we achieve an overall accuracy of 65.7%: 100.0 % for both English and Spanish texts, while only 40 % for Greek texts; evaluated on the test corpus, we achieve an overall accuracy of 68.2%, and roughly the same performance across three languages. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it