Improved BoW-BoC Indexing for Short Texts Using Large Language Models
Bibliographic record
Abstract
Document indexing is a critical component of effective information retrieval systems.However, when dealing with short texts containing limited context and domain-specific terminology, traditional indexing methods often fall short, resulting in low-quality retrieval performance.Traditional Bag-of-Words (BoW) representations that rely solely on document terms are insufficient in this case.This paper introduces an enhanced BoW-BoC indexing approach that leverages generative AI to extract high-level contextual concepts from short documents.These AI-generated concepts form a Bag-of-Concepts (BoC) representation, which is integrated with the BoW model to enrich document representations.During retrieval, the FastText embedding model is used to semantically match query terms with the combined BoW and BoC representations.Experimental results show that the proposed method significantly improves indexing and retrieval performance for semantically sparse short texts, without relying on static knowledge structures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".