Satellite Workshop On Language, Artificial Intelligence \nand Computer Science for Natural Language Processing Applications (LAICS-NLP): Discovery of Meaning from Text
Bibliographic record
Abstract
This paper proposes a novel method to disambiguate important words from a collection of documents. The \nhypothesis that underlies this approach is that there is a \nminimal set of senses that are significant in characterizing a context. We extend Yarowsky’s one sense \nper discourse [13] further to a collection of related \ndocuments rather than a single document. We perform \ndistributed clustering on a set of features representing \neach of the top ten categories of documents in the \nReuters-21578 dataset. Groups of terms that have a \nsimilar term distributional pattern across documents were \nidentified. WordNet-based similarity measurement was \nthen computed for terms within each cluster. An \naggregation of the associations in WordNet that was \nemployed to ascertain term similarity within clusters has \nprovided a means of identifying clusters’ root senses.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".