A Novel Class-Based Data Fusion Technique for Information Retrieval
Bibliographic record
Abstract
Abstract — Data fusion in information retrieval combines the results from multiple retrieval models or document representations. The achievement of data fusion technique is dependent on the quality of the inputs; classical data fusion techniques fail to improve the retrieval if the quality of the retrieval results varies from low to high quality. In order to tackle this problem, in this paper we address the issue of high variation among the retrieval strategies or document representations which affect the combination of their outputs. Our investigation on the MALACH speech collection – in which different segment representations are available – shows that neither the classical data fusion (CombSUM) nor the weighted version (WCombSum) improve the retrieval. We propose a novel class-based data fusion technique to deal with this issue. The segments retrieved by models based on different document representations are classified according to the quality of the segment into three classes: high, intermediate, and low quality class; then the similarity scores of each segment are fused using the classical CombSUM. Our experimental results show that the new technique is significantly better than CombSUM or WCombSUM in combing results with high quality variation. Index Terms—Information storage and retrieval, searching spontaneous speech transcriptions, data fusion. I.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".