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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".