Lexical Chunks Formulaic Sequences and Yukuai: Study of Terms and Definitions of English Multiword Units
Bibliographic record
Abstract
According to the theory of mental lexicon, lexical chunks refer to the multiword units with chunking effects while being processed in utterences. Language acquisition studies hold that formulaic sequences undertake more pragramatic functions bearing more conceptual processing and cultural information. There are some overlaps in the two terms. In the SLA studies in China, researchers attempted to use the coined term Cikuai to be the substitute of these two literally-translated terms—Cihui Zukuai for lexical chunks in Chinese and Chengshi Yu for formulaic sequences in Chinese. This paper proposes that lexical chunks and formulaic sequences have respective linguistic and cognitive features, which direct L1 and L2 speakers to process lexico-semantic multiword units in discourse in different ways. They are the subordinate terms of multiword units in English. This paper claims that the present terms can refer to holistically processed multiword units due to their formulaic and chunking effects.The significant differences lie in their degree of compositionality and semantic productivity. The lexical chunks have higher compositionality and semantic transparency, whereas the formulaic sequences are dynamic lexico-semantic multiword units, which offer exemplars instead of chunks for the reconstruction of lexical items in certain discourses. With regard to the lexical features of meaning extension, recursion and creativeness, we figure out their working definitions and come to the conlusion that Yukuai is not a good terminology to cover all the features entailed in them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".