Measuring the Extent of the Synonym Problem in Full-Text Searching
Bibliographic record
Abstract
Objective – This article measures the extent of the synonym problem in full-text searching. The synonym problem occurs when a search misses documents because the search was based on a synonym and not on a more familiar term. Methods – We considered a sample of 90 single word synonym pairs and searched for each word in the pair, both singly and jointly, in the Yahoo! database. We determined the number of web sites that were missed when only one but not the other term was included in the search field. Results – Depending upon how common the usage is of the synonym, the percentage of missed web sites can vary from almost 0% to almost 100%. When the search uses a very uncommon synonym ("diaconate"), a very high percentage of web pages can be missed (95%), versus the search using the more common term (only 9% are missed when searching web pages for the term "deacons"). If both terms in a word pair were nearly equal in usage ("cooks" and "chefs"), then a search on one term but not the other missed almost half the relevant web pages. Conclusion – Our results indicate great value for search engines to incorporate automatic synonym searching not only for user-specified terms but also for high usage synonyms. Moreover, the results demonstrate the value of information retrieval systems that use controlled vocabularies and cross references to generate search results.
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 | 0.021 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".