Translating English Polysemous Words: A Systematic Literature Review
Bibliographic record
Abstract
English polysemous words usually have semantically related meanings that are based on a central conceptual basis. Their translation is particularly challenging due to their semantic complexity, context dependency and cross-linguistic structural and cultural divergence. However, systematic research on this topic remains limited, particularly in resolving cross-linguistic ambiguity. The aim of this study is to investigate the main challenges in translating English polysemous words between different language pairs and to explore the strategies used by translators to resolve ambiguity. A systematic review of 397 publications indexed in the Scopus and Web of Science databases (as of 31 May 2025) was conducted, with 16 studies selected using the PRISMA framework. The findings show that: (1) translation challenges arise mainly from the interactions between linguistic structures and cultural semantics, especially in legal and medical contexts; (2) translators employ combined strategies such as contextual inference, semantic refinement, synonymous substitution, cultural adjustment, and technological assistance; (3) existing classification frameworks are often inadequate in practise and need to be adapted to specific genres and cultural contexts; (4) strategies are evolving towards integrated, context-sensitive and technology-driven models; (5) relevant research is increasingly interdisciplinary and incorporates insights from linguistics, computational linguistics and cognitive science. This review advances the understanding of English polysemous word translation and provides theoretical and practical insights for researchers and practitioners.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".