Bayesian Folding-In Using Generalized Dirichlet and Beta-Liouville Kernels for Information Retrieval
Bibliographic record
Abstract
Probabilistic latent semantic indexing (PLSI) has been proposed to represent textual documents as mixture proportions of latent topics. Compared to the standard latent semantic indexing (LSI), PLSI has a solid statistical foundation. However, the need of folding new documents into the latent topic space led to the definition of PLSI folding-in. Previous studies have shown that in case of short queries, poor vocabulary results in small sample of words with non-zero frequencies. Therefore, PLSI folding-in tends to produce topic mixtures that are predominated by a single latent aspect. As a result, folding-in is unable to take into consideration different mapping. Thus, Bayesian folding-in was introduced to involve the topic mixtures of the known document and the mixture proportions of topics for a new document were estimated by maximizing the posterior. Hence, the prior was defined as a kernel density estimate using a Dirichlet distribution. Although Bayesian folding-in overcomes PLSI folding-in, there are still some drawbacks since the Dirichlet distribution has negative covariance structure that makes it restrictive, especially in the case of count data. To improve previous works, we propose using generalized Dirichlet (GD) distribution and Beta-Liouville (BL) distribution as kernel densities in a Bayesian framework for information retrieval.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 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".