A distributional account of the semantics of multiword expressions
Bibliographic record
Abstract
The lexical status of multiword expressions (MWEs), such as make a decision and shoot the breeze, has long been a matter of debate. Although MWEs behave much like phrases on the surface, it has been argued that they should be treated like words because their components together form a single unit of meaning. However, MWEs are not a homogeneous lexical category, but rather can have distinct semantic and syntactic properties. For example, the overall meaning of an MWE may vary in how much it diverges from the combined contribution of its constituent parts, with make a decision, e.g., having a strong relation to decide, while shoot the breeze is entirely idiomatic. In order to understand whether and how MWEs should be represented in a (computational) lexicon, it is necessary to look into the relationship between the underlying semantic properties of these expressions and their surface behaviour. We examine several properties of MWEs pertaining to their semantic idiosyncrasy, and relate them to the distributional behaviour of MWEs in their actual usages. Accordingly, we propose statistical measures for quantifying each property, which we then use for separating different types of MWEs that require different treatment within a lexicon. 1
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".