The role of constituents in multiword expressions
Bibliographic record
Abstract
Multiword expressions (MWEs), such as noun compounds (e.g. nickname in English, and Ohrwurm in German), complex verbs (e.g. give up in English, and aufgeben in German) and idioms (e.g. break the ice in English, and das Eis brechen in German), may be interpreted literally but often undergo meaning shifts with respect to their constituents. Theoretical, psycholinguistic as well as computational linguistic research remain puzzled by when and how MWEs receive literal vs. meaning-shifted interpretations, what the contributions of the MWE constituents are to the degree of semantic transparency (i.e., meaning compositionality) of the MWE, and how literal vs. meaning-shifted MWEs are processed and computed. This edited volume presents an interdisciplinary selection of seven papers on recent findings across linguistic, psycholinguistic, corpus-based and computational research fields and perspectives, discussing the interaction of constituent properties and MWE meanings, and how MWE constituents contribute to the processing and representation of MWEs. The collection is based on a workshop at the 2017 annual conference of the German Linguistic Society (DGfS) that took place at Saarland University in Saarbrücken, Germany
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".