A simple term frequency transformation model for effective pseudo relevance feedback
Why this work is in the frame
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Bibliographic record
Abstract
Pseudo Relevance Feedback is an effective technique to improve the performance of ad-hoc information retrieval. Traditionally, the expansion terms are extracted either according to the term distributions in the feedback documents; or according to both the term distributions in the feedback documents and in the whole document collection. However, most of the existing models employ a single term frequency normalization mechanism or criteria that cannot take into account various aspects of a term's saliency in the feedback documents. In this paper, we propose a simple and heuristic, but effective model, in which three term frequency transformation techniques are integrated to capture the saliency of a candidate term associated with the original query terms in the feedback documents. Through evaluations and comparisons on six TREC collections, we show that our proposed model is effective and generally superior to the recent progress of relevance feedback models.
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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 | 0.000 | 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