Incremental maintenance of length normalized indexes for approximate string matching
Bibliographic record
Abstract
Approximate string matching is a problem that has received a lot of attention recently. Existing work on information retrieval has concentrated on a variety of similarity measures TF/IDF, BM25, HMM, etc.) specifically tailored for document retrieval purposes. As new applications that depend on retrieving short strings are becoming popular(e.g., local search engines like YellowPages.com, Yahoo!Local, and Google Maps) new indexing methods are needed, tailored for short strings. For that purpose, a number of indexing techniques and related algorithms have been proposed based on length normalized similarity measures. A common denominator of indexes for length normalized measures is that maintaining the underlying structures in the presence of incremental updates is inefficient, mainly due to data dependent, precomputed weights associated with each distinct token and string. Incorporating updates usually is accomplished by rebuilding the indexes at regular time intervals. In this paper we present a framework that advocates lazy update propagation with the following key feature: Efficient, incremental updates that immediately reflect the new data in the indexes in a way that gives strict guarantees on the quality of subsequent query answers. More specifically, our techniques guarantee against false negatives and limit the number of false positives produced. We implement a fully working prototype and illustrate that the proposed ideas work really well in practice for real datasets.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".