Bibliographic record
Abstract
Abstract This article studies the issue of argument realization by preposition structures. By examining the preposition structures that are marked as frame elements in FrameNet, the article attempts to give corpus-based attestations to the hypothesized link between deep semantic arguments and their surface syntactic representations. Problems addressed in this article include how argument realization by preposition structures can be predictable from the target lexical unit and the frame it evokes, and why some noncentral prepositions get selected in the argument realization options. The investigation is primarily inspired by Fillmore's work in frame semantics. The source data for this study is derived from a preposition knowledge base that we have recently built by extracting all the semantically annotated preposition structures in FrameNet. The analysis shows that while there are various semantic–syntactic mapping possibilities, for most semantic arguments, the tendency of using central prepositions in their realization expressions is very strong. This is a clear indication that some preposition structures are linked to certain semantic arguments more than they are to others. A similar experiment was conducted using the annotated PropBank corpus to corroborate the supporting evidence found in FrameNet. The results of this study, together with the syntactic–semantic mapping lists of preposition structures can provide raw linguistic data for the study of preposition semantics, lexicography, argument realization, word sense disambiguation, and natural language understanding.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".