Multiword noun compound bracketing using Wikipedia
Bibliographic record
Abstract
This research suggests two contributions in relation to the multiword noun compound bracketing problem: first, demonstrate the usefulness of Wikipedia for the task, and second, present a novel bracketing method relying on a word association model. The intent of the association model is to represent combined evidence about the possibly lexical, relational or coordinate nature of links between all pairs of words within a compound. As for Wikipedia, it is promoted for its encyclopedic nature, meaning it describes terms and named entities, as well as for its size, large enough for corpus-based statistical analysis. Both types of information will be used in measuring evidence about lexical units, noun relations and noun coordinates in order to feed the association model in the bracketing algorithm. Using a gold standard of around 4800 multiword noun compounds, we show performances of 73% in a strict match evaluation, comparing favourably to results reported in the literature using unsupervised approaches.
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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 | 0.000 | 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 | 0.001 | 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 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".