<b>Cross-Language Information Retrieval Jian-Yun Nie</b> (University of Montreal) San Rafael, CA: Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 8), 2010, xv+125 pp; paperbound, ISBN 978-1-59829-863-5, $40.00; ebook, ISBN 978-1-59829-864-3, $30.00 or by subscription
Bibliographic record
Abstract
Cross-Language Information Retrieval is a compact book introducing a branch of information retrieval that has gained considerable research interest since the dawn of the World Wide Web in the mid 1990s. Information retrieval is generally concerned with the problem of finding documents within a large collection that are relevant to a given input query. Whereas the original formulation of IR assumes that queries and documents are written in the same language, cross-language IR (CLIR) presumes instead that they are written in two different languages. If the collection contains documents in more languages, then we refer to multi-lingual IR (MLIR), which is typically solved with multiple instances of CLIR. Recently, other variations on the theme have been proposed that address non-textual documents, such as image, music, and speech retrieval. An interesting application of CLIR is the retrieval of images that are provided with textual descriptions in any language.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".