Persian compounds in the mental lexicon
Bibliographic record
Abstract
Compound words exhibit properties of both single words and phrases, raising the question of the extent to which compounds are processed as single units or as word combinations. Most studies have addressed this in Germanic languages (English, German and Dutch) which have the similar compound structure of modifier-head ordering. To see whether this limits our understanding of compound word processing and to examine compound decomposition in another language, we presented Persian stimuli auditorily in a paradigm involving typing out stimuli. We examined the effects of semantic transparency, modifier-head ordering and the potential differences between attached compounds written without spaces and those with a space between the constituents. We report the inter-keystroke-interval times, yielding letter-by-letter production of compound structures produced by 31 native speakers of Persian. Results analyzed in a linear mixed-model regression analysis suggested that, for all compounds, typing speed is slowed at the boundary between the constituents of Persian compound words. These effects, which we interpret to be evidence of morphological decomposition, were present for both semantically transparent and opaque compounds, for both head-initial and head-final compounds, and for both attached and spaced compounds. We observed greater morphological decomposition effects in semantically transparent (versus opaque) compounds. We also observed that the way transparency influences the degree of decomposition is moderated by headedness. Thus, this first report for the written production of compound words confirms previous observations of significant decomposition at morphological boundaries in English compounds, but with variation specific to Persian.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".