Beyond Plain and Extra-Grammatical Morphology: Echo-Pairs in Hungarian
Bibliographic record
Abstract
[t͡sit͡sɒ-mit͡sɒ] "cat.dim"). Echo-pairs are commonly seen as an example of extra-grammatical morphology in the literature. Our goal in looking at this phenomenon is to gain a better understanding of the morphological mechanisms underlying extra-grammatical phenomena and shed new light on the distinction between plain and extra-grammatical morphology. We analyze data from (a) a collection of echo-pairs extracted from a large corpus of online texts and (b) a large-scale online nonce-word experiment with close to 1,500 participants. Our results reveal two key phonological patterns in the data and some additional systematic variation across words and experimental stimuli. We compare two different models of morphology, the Minimal Generalization Learner and the Generalized Context Model, in terms of their ability to capture this variation. We find that echo-pair formation is best captured by lexicon-oriented models such as the Generalized Context Model, but only when they rely on a structured similarity metric that encodes broader generalizations about the data. Our results do not support a clear-cut distinction between extra-grammatical and plain morphological processes, and we suggest that some of the peculiar characteristics of extra-grammatical phenomena such as echo-pair formation may simply follow from their special function and the limited set of contexts in which they appear.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".