Binary Independence Language Model in a Relevance Feedback Environment
Bibliographic record
Abstract
Model construction is a kind of knowledge engineering, and building retrieval models is critical to the success of search engines. This article proposes a new (retrieval) language model, called binary independence language model (BILM). It integrates two document-context based language models together into one by the log-odds ratio where these two are language models applied to describe document-contexts of query terms. One model is based on relevance information while the other is based on the non-relevance information. Each model incorporates link dependencies and multiple query term dependencies. The probabilities are interpolated between the relative frequency and the background probabilities. In a simulated relevance feedback environment of top 20 judged documents, our BILM performed statistically significantly better than the other highly effective retrieval models at 95% confidence level across four TREC collections using fixed parameter values for the mean average precision. For the less stable performance measure (i.e. precision at the top 10), no statistical significance is shown between the different models for the individual test collections although numerically our BILM is better than two other models with a confidence level of 95% based on a paired sign test across the test collections of both relevance feedback and retrospective experiments.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".