Thomson Legal and Regulatory at NTCIR-4: Monolingual and Pivot-Language Retrieval Experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thomson Legal and Regulatory participated in the CLIR task of the NTCIR-4 workshop. We submitted formal runs for monolingual retrieval in Japanese, Chinese and Korean. Our bilingual runs from Chinese and Korean to Japanese rely on English as a pivot lan- guage. During our monolingual experiments, we compared building stopword lists using query logs to building stopword lists from collection statistics with further manual editing. We investigated decompounding for Korean, more precisely partial credit of compound parts. Finally we incorporated pseudo-relevance feed- back in our Japanese runs. Our bilingual approach was an experiment to con- struct a system within a short timeframe using publi- cally available resources. The low quality of retrieval suggests that such an approach is not viable in a real environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it