Computational Linguistic Interpretation and Modeling of Dress Metaphors in Ming Dynasty Novel Texts
Bibliographic record
Abstract
Dress metaphor is a very important way of expression in the novel text of Ming Dynasty, and the recognition and interpretation of the metaphor play a very important role in really understanding the novel text.This paper proposes a dress metaphor recognition model based on Transformer and graph convolutional neural network, and a dress metaphor interpretation method based on Seq2seq framework.The apparel metaphor recognition model performs feature extraction of global and local information of apparel metaphor sentences by Transformer.Graph Convolutional Neural Network is utilized to obtain syntactic structure information and sentence dependencies, in order to complete multi-word dress metaphor recognition.Then the obtained deep metaphor features and syntactic structure information of the sentence are input to the classification layer.The metaphor decoding method carries out costume metaphor understanding through the encoder-decoder, which chooses the LSTM network structure for both encoder and decoder to better obtain the semantic features of the novel text.The dress metaphor recognition model improved the recognition correctness on the dataset by 17.97% and 7.28%.The dress metaphor interpretation method based on the Seq2seq framework elaborates the interpretation content and can more accurately interpret the dress metaphors in Ming Dynasty novels.It verifies the practicality of the metaphor recognition and interpretation model in this paper in the task of interpreting dress metaphors in Ming Dynasty novel texts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".