Verb-based restrictions on noun incorporation across languages
Bibliographic record
Abstract
Abstract Although some characteristics of incorporating verbs and non-incorporating verbs have been proposed in previous studies, little systematic cross-linguistic research has been done on restrictions on the types of verbs that incorporate nouns. Knowledge about possible verb-based restrictions on noun incorporation may, however, provide important insights for theoretical approaches to noun incorporation, in particular regarding the question to what extent incorporation is a lexical or a syntactic process, and whether and how languages may vary in this respect. This paper therefore investigates to what extent languages restrict noun incorporation to particular verbs and what types of restrictions appear to be relevant cross-linguistically. The study consists of two parts: an explorative typological survey based on descriptive sources of 50 incorporating languages, and a more detailed investigation of incorporating verbs in corpus data from a sample of eight languages, guided by a questionnaire. The results demonstrate that noun incorporation is indeed restricted in terms of which verbs allow this construction within and across languages. The likelihood that a verb can incorporate is partly determined by its degree of morphosyntactic transitivity, but the attested variation across verbs and across languages shows that purely lexical restrictions play an important role as well.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".