Embedded Spherical Topic Models for Supervised Learning
Bibliographic record
Abstract
Probabilistic topic models are powerful techniques for analyzing and understanding large collections of text documents to learn meaningful patterns of words. Their supervised extensions also capture topics conditioned on the response metadata associated with each document such as user rating. However, inferring such information from data often comes at the detriment of topics quality, leading to uninterpreted and meaningless topics. In this paper, we propose a novel Supervised-Embedded Spherical Topic Model (S-ESTM) that balances two goals: interpretable and coherent topics explaining the data and accurate prediction of the associated response values. Our model combines word embeddings and knowledge graph embeddings to effectively encode the semantic information of text and the related background knowledge to guide the inference of supervised topics. In S-ESTM, document constituents are drawn as points on spherical manifolds along with topics using the von Mises-Fisher distribution. Efficient variational inference methods for posterior approximation and latent parameter estimation are derived and various empirical studies on real-world datasets are also provided. Our experiments demonstrate that our model can discover discriminative and coherent topical patterns associated with regression tasks, while achieving improved prediction quality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".