Interactive text document clustering using feature labeling
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
We propose an interactive text document method, which is based on term labeling. The algorithm asks the user to cluster the top keyterms associated with document clusters iteratively. The keyterm clusters are used to guide the clustering method. Rather than using standard clustering algorithms, we propose a new text clusterer using term clusters. Terms that exist in a document corpus are clustered. Using a greedy approach, the term clusters are distilled in order to remove non-discriminative general terms. We then present a heuristic approach to extract seed documents associated with each distilled term cluster. These seeds are finally used to cluster all documents. We compared our interactive term labeling to a baseline interactive term selection algorithm on some real standard text datasets. The experiments show that with a comparable amount of user effort, our term labeling is more effective than the baseline term selection method.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 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