A BoW-BoC Indexing Method to Enhance Business-Related Document Representation and Retrieval
Bibliographic record
Abstract
Document indexing is crucial for efficient information retrieval systems.However, when documents are business-related and contains extensive figures and domain-specific terms, retrieving such documents poses challenges due to their lack of semantic context.Traditional Bag-of-Words (BoW) representation relying on word lists extracted from documents has limitations in such cases since it is based on a low-context approach.To address this issue, a BoW-BoC indexing method is proposed.This method utilizes a lexicon tailored to document contexts to associate BoW representations with Bag-of-Concepts (BoC), providing the necessary semantics for improved retrieval.The LSA model is used in conjunction with cosine similarity to automatically identify associations between document representations and lexicon concepts grouped in topics within a low-dimensional space.This BoW-BoC association is leveraged during document retrieval, supported by a weighted scheme intended to balance the contributions of both representations.Initial experiments conducted on an open document collection have shown promising results, demonstrating the potential effectiveness of the BoW-BoC indexing method for business-related documents.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".