Approaches to High Accuracy Retrieval: Phrase-Based Search Experiments in the HARD Track.
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
Our main research focus this year was on the use of phrases (or multi-word units) in query expansion. Multi-word units (MWUs) comprise a number of lexical units, such as named entities (“United Nations”), nominal compounds (“amusement park”) and phrasal verbs (‘check in’). Although MWUs can belong to different parts of speech, our focus was on nominal MWUs. We used a combined syntactico-statistical approach for selecting nominal MWUs. In the first selection pass we obtained valid noun phrases, and in the second pass we used statistical measures to select strongly bound MWUs. We have experimented with using two statistical measures of selecting MWUs from text: the C-value (Frantzi and Ananiadou 1996, Vintar 2004) and the Log-Likelihood ratio (Dunning 1993). Selected MWUs were then suggested to the user for interactive query expansion. Two main research questions were studied in these experiments:
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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