Syntax and Semantics of It-Clefts: A Tree Adjoining Grammar Analysis
Bibliographic record
Abstract
In this paper, we examine two main approaches to the syntax and semantics of it-clefts as in ‘It was Ohno who won’: an expletive approach where the cleft pronoun is an expletive and the cleft clause bears a direct syntactic or semantic relation to the clefted constituent, and a discontinuous constituent approach where the cleft pronoun has a semantic content and the cleft clause bears a direct syntactic or semantic relation to the cleft pronoun. We argue for an analysis using Tree Adjoining Grammar (TAG) that captures the best of both approaches. We use Tree-Local Multi-Component Tree Adjoining Grammar to propose a syntax of it-clefts and Synchronous Tree Adjoining Grammar (STAG) to define a compositional semantics on the proposed syntax. It will be shown that the distinction TAG makes between the derivation tree and the derived tree, the extended domain of locality characterizing TAG and the direct syntax–semantics mapping characterizing STAG allow for a simple and straightforward account of the syntax and semantics of it-clefts, capturing the insights and arguments of both the expletive and the discontinuous constituent approaches. Our analysis reduces the syntax and semantics of it-clefts to copular sentences containing definite description subjects, such as ‘The person that won is Ohno’. We show that this is a welcome result, as evidenced by the syntactic and semantic similarities between it-clefts and the corresponding copular sentences. 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".