Bibliographic record
Abstract
Indexing is a crucial technique for dealing with the massive amount of data present on the web. In our third participation in the web track at TREC 2012, we explore the idea of building an efficient query-based indexing system over Web page collection. Our prototype explores the trends in user queries and consequently indexes texts using particular attributes available in the documents. This paper provides an in-depth description of our approach for indexing web documents efficiently; that is, topics available in the web documents are discovered with the assistance of knowledge available in Wikipedia. The welldefined articles in Wikipedia are shown to be valuable as a training set when indexing Webpages. Our complex index structure also records information from titles and urls, and pays attention to web domains. Our approach is designed to close the gaps in our approaches from the previous two years, for some queries. Our framework is able to efficiently index the 50 million pages available in the subset B of the ClueWeb09 collection. Our preliminary experiments on the TREC 2012 testing queries showed that our indexing scheme is robust and efficient for both indexing and retrieving relevant web pages, for both the ad-hoc and diversity task.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".