Analysis of Natural Imagery in Tang Poetry Texts Based on Principal Component Modeling
Bibliographic record
Abstract
Tang poetry, as a treasure of ancient Chinese literature, contains a wealth of natural imagery, which not only add to the picture sense of Tang poetry, but are also important carriers of the poet's emotions and thoughts.The study outlines the nature imagery from the perspective of Tang poetry, as well as the key elements and intrinsic connections among them, and borrows k-means clustering to categorize the nature imagery groups.In addition, the study improves the principal component model by using index homogenization, homogenization, and entropy weighting, so that it achieves the best dimensionality reduction effect while guaranteeing the integrity of the data of Tang poetry text.The F1 value of SVM and KNN classifiers for classifying the natural imagery and emotional expression of Tang poetry text is more than 0.9 after dimensionality reduction of the method in this paper, which is a good classification performance.Cluster analysis divides the natural imagery of Tang poetry into astronomical imagery, landscape imagery, and animal imagery, which account for 38%, 53%, and 9%, respectively."Old times -bright moon", "Thinking -slanting sun", "Looking back -west wind", "the end of the world -west wind" natural discourse is more likely to form word clusters in the natural imagery of Tang poetry.The analysis of principal component model shows that poets are more willing to express their emotions through natural imagery, and the proportion of neutral emotional expression is 5.17% to 7.43%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".