Admitting that admitting verb sense into corpus analyses makes sense
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
Linguistic and psycholinguistic research has documented that there exists a close relationship between a verb’s meaning and the syntactic structures in which it occurs, and that learners and comprehenders take advantage of this relationship both in acquisition and in processing. We address implications of these facts for issues in structural ambiguity resolution, arguing that comprehenders are sensitive to meaning-structure correlations based not on the verb itself but on its specific senses, and that they exploit this information on-line. We demonstrate that individual verbs show significant differences in their subcategorisation profiles across three corpora, and that cross-corpora bias estimates are much more stable when sense is taken into account. Finally, we show that consistency between sense-contingent subcategorisation biases and experimenters’ classifications largely predicts results of recent experiments. Thus comprehenders learn and exploit meaning-form correlations at the level of individual verb senses, rather than the verb in the aggregate.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it