Automatic Verb Classification Using a General Feature Space
Why this work is in the frame
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Bibliographic record
Abstract
Automatic Verb Classi cation Using a General Feature Space Eric Joanis Master of Science Graduate Department of Computer Science University of Toronto 2002 We develop a general feature space that can be used for the semantic classi cation of English verbs. We design a technique to extract these features from a large corpus of English, while trying to maintain portability to other languages|the only languagespeci c tools we use to extract our core features are a part-of-speech tagger and a partial parser. We show that our general feature space reduces the chance error rate by 40% or more in ten experiments involving from two to thirteen verb classes. We also show that it usually performs as well as features that are selected using speci c linguistic expertise, and that it is therefore unnecessary to manually do linguistic analysis for each class distinction of interest. Finally, we consider the use of an automatic feature selection technique, stepwise feature selection, and show that it does not work well with our feature space.
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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 | 0.000 |
| 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