Measure-driven keyword-query expansion
Bibliographic record
Abstract
User generated content has been fueling an explosion in the amount of available textual data. In this context, it is also common for users to express, either explicitly (through numerical ratings) or implicitly, their views and opinions on products, events, etc. This wealth of textual information necessitates the development of novel searching and data exploration paradigms. In this paper we propose a new searching model, similar in spirit to faceted search, that enables the progressive refinement of a keyword-query result. However, in contrast to faceted search which utilizes domain-specific and hard-to-extract document attributes, the refinement process is driven by suggesting interesting expansions of the original query with additional search terms. Our query-driven and domain-neutral approach employs surprising word co-occurrence patterns and (optionally) numerical user ratings in order to identify meaningful top- k query expansions and allow one to focus on a particularly interesting subset of the original result set. The proposed functionality is supported by a framework that is computationally efficient and nimble in terms of storage requirements. Our solution is grounded on Convex Optimization principles that allow us to exploit the pruning opportunities offered by the natural top- k formulation of our problem. The performance benefits offered by our solution are verified using both synthetic data and large real data sets comprised of blog posts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".