Translation strategy for nominal phrases: analysis of morphosemantic errors
Bibliographic record
Abstract
This article discusses translation strategies related to Morphosemantic Errors. The purpose of this research is to identify nominal phrases, then each structure of nominal phrases is described into three forms of nominal phrases, namely coordinative endocentric phrases, attributive endocentric phrases, and fixed phrases and analyze the strategies used by the translator in translating this short story. This study used descriptive qualitative method. The results of the analysis show that the translator uses various strategies in translating, namely transfer, naturalization, cultural equivalents, functional equivalents, descriptive equivalents, synonyms, comprehensive equivalents, shifting or transposition, modulation, compensation, translation of familiar words, component analysis, paraphrasing, reduction, expansion. In addition, there are some deviations to the nominal phrase. To reveal morphosemantic errors in the Indonesian translation text. Language is used by humans in the world to interact with others. It is a system of arbitrary sound symbols, used by members of social groups to identify themselves, communicate, and work together". Every country has a different language, for example there are several languages whose sentences start with a noun or are also called nouns. The words that are included in nouns are people, animals, things and concepts such as in English and in Chinese.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".