Statistical query translation models for cross-language information retrieval
Bibliographic record
Abstract
Query translation is an important task in cross-language information retrieval (CLIR), which aims to determine the best translation words and weights for a query. This article presents three statistical query translation models that focus on the resolution of query translation ambiguities. All the models assume that the selection of the translation of a query term depends on the translations of other terms in the query. They differ in the way linguistic structures are detected and exploited. The co-occurrence model treats a query as a bag of words and uses all the other terms in the query as the context for translation disambiguation. The other two models exploit linguistic dependencies among terms. The noun phrase (NP) translation model detects NPs in a query, and translates each NP as a unit by assuming that the translation of a term only depends on other terms within the same NP. Similarly, the dependency translation model detects and translates dependency triples, such as verb-object, as units. The evaluations show that linguistic structures always lead to more precise translations. The experiments of CLIR on TREC Chinese collections show that all three models have a positive impact on query translation and lead to significant improvements of CLIR performance over the simple dictionary-based translation method. The best results are obtained by combining the three models.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".