Performance of information retrieval models using term co-occurrences
Why this work is in the frame
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Bibliographic record
Abstract
Many advanced models have been developed for information retrieval in recent years. These models are built on various artificial intelligence paradigms to improve the precision of the retrieval. Most of them exploit some form of term co-occurrences to improve retrieval quality. In this paper, we compare the retrieval performance of five of these models: the Extended Boolean model, the Generalized Vector Space model, the Frequent Set model, the Rough Set model and a Genetic-Based model. These models are tested on three sub-collections from TREC (Text REtrieval Conference). We analyze the specificity of the models regarding the form of co-occurrences introduced and report on the retrieval performance and the scalability of each model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it