A Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model
Bibliographic record
Abstract
Recently neural information retrieval systems have spurred many successful applications. Retrieval model to obtain a candidate document collection in the first retrieval stage, then use BERT to sort the candidate documents. Generally, the sentence score or paragraph score obtained using BERT is integrated into the document score to get the final ranking result. Semantic similarity is less often used to select query extensions and integrate semantic information into pseudo-relevance feedback. We propose a new strategy in this paper, selecting query extensions with semantic information using the BERT model. Incorporating semantic information weights into traditional pseudo-relevance feedback can better alleviate problems such as word polysemy and multi-word synonymy. Improve the performance of the retrieval system and return more accurate documents. The experimental results demonstrate that the query extensions selected by incorporating semantic information can help return more accurate results and improve the accuracy of the retrieval system, and the results of MAP and P@10 can prove the validity and feasibility of our proposed model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".