A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval
Bibliographic record
Abstract
Pre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo-relevant documents, followed by re-ranking with a pre-trained model. However, the semantic information captured by pre-trained models from sentences or passages is usually only applied to document ranking, with limited use in query expansion. In fact, the semantic information within pseudo-relevant documents plays a critical role in selecting appropriate query expansion terms. Therefore, this paper proposes a novel approach that leverages pre-trained models to extract multi-dimensional semantic information from pseudo-relevant documents, offering more possibilities for query expansion. First, traditional sparse retrieval methods are used in the initial retrieval stage to ensure efficiency, and term-level weights are calculated based on statistical information. Then, the pre-trained model encodes both the query and the sentences and passages from the documents, extracting sentence-level and passage-level semantic similarities to the query. Finally, these semantic weights are combined with the term-level weights to generate an improved query for the second retrieval round. We conducted experiments on five TREC datasets and a medical dataset, showing improvements in official metrics such as MAP and P@10. The results demonstrate the effectiveness of utilizing multi-dimensional semantic information from pseudo-relevant documents to optimize query expansion. This study offers new insights into how the semantic information of pseudo-relevant documents can be effectively harnessed to enhance retrieval performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".