Smoothed Generalized Dirichlet: A Novel Count-Data Model for Detecting Emotional States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we propose novel approaches to deal with the problem of burstiness, the challenge of count-data sparseness, and the curse of dimensionality. We introduce a smoothed generalized Dirichlet distribution that is a smoothed variant of the generalized Dirichlet distribution and a generalization of the smoothed Dirichlet. We provide different learning methods based on mixture models and agglomerative clustering-based geometrical information: Kullback–Leibler divergence, Fisher metric, and Bhattacharyya distance. Moreover, we show that the new smoothed generalized Dirichlet could be considered as a prior to the multinomial, which generates a new distribution for count data that we call the smoothed generalized Dirichlet multinomial. In particular, we present an approximation based on Taylor series expansion for better performance and optimized running time in the case of high-dimensional count data. The proposed models are evaluated through two emotion detection applications: disaster-tweet-related emotions and pain intensity estimation. Experiments show the efficiency and the robustness of our approaches when dealing with texts, videos, and images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it