On Chinese EFL Learners’ Homonym Processing in Relation to Their Organization of L2 Mental Lexicon
Bibliographic record
Abstract
Based on the models of lexical ambiguity resolution and the organization of L2 mental lexicon proposed by previous researchers in recent years, this paper aimed at investigating the correlation between the Chinese EFL learners’ homonym processing in relation to their organization of L2 mental lexicon through cross-modal sentence-priming lexical decision tasks and word association tasks completed by both English major and non-English major students. The results showed that there is a positive correlation between the learners’ organization of L2 mental lexicon and their performance on English homonym processing. Inferential statistic analysis indicates that learners whose L2 mental lexicon is more syntagmatically related can process the English homonym more effectively. They can use contextual cues (semantic and syntactic information) to deactivate inappropriate meanings while low proficiency learners may be slower and less effective in using the sentence context cues needed for disambiguation. Therefore, it has been tentatively indicated that effective spreading activation of a word during reading and listening is important and helpful for learners’ comprehension, and pedagogically suggested that consciously categorizing syntagmatically related words in L2 mental lexicon during teaching and learning new English words can help reconstruct lexical networks so as to correctly and quickly retrieve the words from the mental lexicon in a certain context and better understand the meaning of the whole context in reading and listening.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".