A self-organizing map for concept classification in information retrieval
Bibliographic record
Abstract
Information retrieval is concerned with the classification processes and the selective recovery of information. Improvements in this field are mainly sought at the core level of the engine's classification capabilities and by query enhancement processes. The later one became the prime interest of researchers since less progress has been made on the former one. Both make substantial use of manual interventions, which results in a less automated overall process. In this paper, we propose a new model based on the self-organizing map paradigm to discover the concepts embedded in a collection of documents. The terms of the corpus are directly classified into concepts, without manual category labelling. Then the concepts serve as a new knowledge representation for information retrieval. This model has been tested on a TREC-6 subcollection (text retrieval conference). As expected, the retrieval using the concepts representation does not outperform the corresponding full term retrieval. It is a step toward terms classification using a self-organizing map and contributes to fully automate the discovery of concepts in text collections.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".