Bibliographic record
Abstract
Keyphrases are useful for a variety of purposes,\nincluding summarizing, indexing, labeling,\ncategorizing, clustering, highlighting, browsing, and\nsearching. The task of automatic keyphrase extraction\nis to select keyphrases from within the text of a given\ndocument. Automatic keyphrase extraction makes it\nfeasible to generate keyphrases for the huge number of\ndocuments that do not have manually assigned\nkeyphrases. A limitation of previous keyphrase\nextraction algorithms is that the selected keyphrases are\noccasionally incoherent. That is, the majority of the\noutput keyphrases may fit together well, but there may\nbe a minority that appear to be outliers, with no clear\nsemantic relation to the majority or to each other. This\npaper presents enhancements to the Kea keyphrase\nextraction algorithm that are designed to increase the\ncoherence of the extracted keyphrases. The approach is\nto use the degree of statistical association among\ncandidate keyphrases as evidence that they may be\nsemantically related. The statistical association is\nmeasured using web mining. Experiments demonstrate\nthat the enhancements improve the quality of the\nextracted keyphrases. Furthermore, the enhancements\nare not domain-specific: the algorithm generalizes well\nwhen it is trained on one domain (computer science\ndocuments) and tested on another (physics documents).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".