Architecture for automatic poetry generation through pattern recognition
Bibliographic record
Abstract
Document representation and topic modelling are important problems for artificial intelligence researchers, with applications ranging from education technology to bioinformatics.Many approaches have been proposed, the majority falling broadly into categories of Statistical Analysis and Natural Language Processing (NLP).This thesis proposes an architecture that optimizes a combination of statistical and linguistic analysis in an unsupervised machine learning environment.The proposed architecture is a design for agile, stable, document modelling.By clustering within the statistical inference algorithm, it reduces the computational cost of time and space associated with conventional classifying algorithms such as K-means, increasing the threshold for size and frequency of aggregate data analysis.This translates to an increased stability for evolution of learning.The architecture builds on the concept of socio-linguistic connections as an inherent combination of statistics and linguistics, and employs well-researched concepts of statistical and linguistic analysis, including embedded sub-manifold analysis.It optimizes both linguistic connections and computational cost.Trials are run with three sets of parameters, and results distributed for volunteer evaluation.Feedback from the evaluation indicates that the proposed architecture produces groups of sentences (poems) with a high degree of social acceptance and response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".