A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning
Bibliographic record
Abstract
In the field of information Retrieval (IR), Pseudo-relevance feedback (PRF) and Query Expansion (QE) techniques have garnered significant attention for their efficacy in enhancing retrieval effectiveness. However, traditional PRF approaches predominantly concentrate solely on pseudo-relevant documents identified during the initial retrieval stage, neglecting the rich semantic information embedded within non-pseudo-relevant documents. This paper introduces an innovative PRF model that integrates soft negative samples and contrastive learning to address this limitation, aiming for a more comprehensive capture and representation of semantics. First, we employ the BM25 algorithm as the baseline retrieval mechanism to accurately pinpoint pseudo-relevant documents from the first stage retrieval and assign weights to their terms. Second, a contrastive learning strategy is introduced to distill semantic features from all documents globally, further refining the semantic weights of terms. To mitigate the risk of information loss associated with soft negative samples, we ingeniously leverage the statistical properties of kernel function to precisely gauge the co-occurrence frequencies between terms, ensuring the preservation of core information and thus obtaining kernel function term co-occurrence weights. Third, we select semantically related terms highly relevant to the query for creating an optimized query by balancing these three weight distributions. Extensive empirical analyses conducted on several TREC datasets demonstrate the practical feasibility of our proposed model. It outperforms baseline models and state-of-the-art technologies on core evaluation metrics such as MAP, P@10, NDCG, and MRR. Deeper comparative experiments and case studies reveal that the expansion terms generated by our model exhibits a deeper level of semantic coherence with the original query, underscoring the dual advantages of the model in both theory and practice. In summary, the model presented herein not only opens a new path at the technical level, but also provides a more accurate and efficient solution for real-world applications in IR.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".