Learning Term Dependency Links Using Information Theoretic Inclusion Measure
Why this work is in the frame
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Bibliographic record
Abstract
An algorithm to identify and remove term redundancy is proposed for text classifiers using ranking-based feature selection. The proposed method employs a normalized mu- tual information, which is called inclusion measure, to es- timate asymmetric dependency between two terms. Based on pair-wise dependency measures, a dependency matrix is constructed. In this paper, an algorithm is proposed to learn term dependency links from term dependency matrix, and visualize the dependency between term in a graph called term dependency tree. All nodes of the tree are categorized into two groups: hubs and links. Any node whose outde- gree is less than two will join the Links group. We show that all link nodes are most likely redundant. We also in- troduce a criterion, which is called substitution cost, to de- cide whether to remove or retain a candidate, redundant term. The proposed approach is applied to four well-known benchmark data sets with a SVM and Rocchio classifier us- ing a set of highly aggressive feature selection schemes. The results show the effectiveness of the proposed method espe- cially when applied to weak classifiers.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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