Bibliographic record
Abstract
Search Engine queries often have duplicate words in the search string. For example user searching for "pizza pizza" a popular brand name for Canadian pizzeria chain. An efficient search engine must return the most relevant results for such queries. Search queries also have pair of words which always occur together in the same sequence, for example “honda accord”, “hopton wafers”, “hp newwave” etc. We will hereafter refer to such pair of words as bigrams. A bigram can be treated as a single word to increase the speed and relevance of results returned by a search engine that is based on inverted index. Terms in a user query have a different degree of importance based on whether they occur inside title, description or anchor text of the document. Therefore an optimal weighting scheme for these components is required for search engines to prioritize relevant documents near the top for user searches. The goal of my project is to improve Yioop, an open source search engine created by Dr Chris Pollett, to support search for duplicate terms and bigrams in a search query. I will also optimize the Yioop search engine by improving its document grouping and BM25F weighting scheme. This would allow Yioop to return more relevant results quickly and efficiently for users of the search engine.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".