The Applicability of Numerical Methods in the Quantitative Study of Syntactic Structure of Chinese Language and Literature
Bibliographic record
Abstract
Under the dual background of the construction of the "new liberal arts" and the digital wave, the interdisciplinary practice of combining humanities and technology continues to develop.Taking a number of Chinese language and literature works as examples, this paper selects language features from the vocabulary and sentence levels, analyzes the syntactic structure of the selected Chinese language and literature works with the help of natural language processing technology and numerical measurement method of language features improved TF-IDF method, and realizes the discussion of the lexical categories of literary works, such as word length, word frequency, word class distribution and word density, as well as the study of sentence categories such as average sentence length, sentence dispersion and sentence class distribution.It is found that most of the utterances of the selected literary works are monosyllabic words and polysyllabic words, the cumulative proportion of both of them is more than 90%, the highest frequency of occurrence is nouns and verbs, both of them are more than 22%, the average sentence length and sentence dispersion do not differ much, and the overall readability of the selected literary works is better, with a free change of syntactic structure and a stronger narrative of the text.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".