Research into the Mental Lexicon Representation of Chinese English Learners Based on Spreading Activation Model
Bibliographic record
Abstract
Nowadays, the main idea regarding the organization of the lexicon is that words are stored in an organized intertwined semantic network. However, relatively little is known about the actual process that takes place during the course of activation production. Therefore, in order to gain a deeper understanding of the problems in question, this study conducted word association test to 150 sophomores in Dalian University of Technology (DUT) and tried to show the internal relations of mental lexicon in data by calculating the word frequency between certain words through a computer program which is written based on the actual calculating steps. And the innovation of this study is to show the abstract lexicon relation in data and illustrate the mental lexicon representation in three-dimensional figures by Netdraw software. Through the study we find: (1) The responses with higher frequency in the first few positions may not ensure themselves high association strength to the stimuli. And the current research also proves that activation of mental lexicon is not a “one stop” process but a linear forward one. (2) The data of association strength obtained from this study may help us convert the abstract lexicon relation into concrete statistical facts and establish representation of the mental lexicon network model. At last, the mechanism of Spreading Activation Model is illustrated and the implications for future English teaching are provided. Key Words: Mental lexicon; Word association test; Mental lexicon representation; Association strength
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".