Applying Probabilistic Thematic Clustering for Classification in the TREC 2005 Genomics Track
Bibliographic record
Abstract
Our group participated in the categorization task of the TREC Genomics Track. We introduced and investigated a cluster-based approach for classifying documents. We first clustered the abstracts of the negative training examples based on their term distribution, then built a classifier to distinguish between each cluster and the set of positive examples. The large number of resulting classifiers (a total of 14-19 classifiers per domain) was combined to categorize the test set. We also conducted experiments for clusterbased feature selection; Rather than select features from the whole negative and positive training sets, we selected features from each of the clusters and took the union of these features as the selected features for representing the whole training and test data. We compared our cluster-based multi-classifier approach against a simple naïve Bayes classification. We also compared the cluster-based feature selection strategy with the commonly used Chi-square-based feature selection. 1.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".