Correlated Topic Modeling for Short Texts in Spherical Embedding Spaces
Bibliographic record
Abstract
With the prevalence of short texts in various forms such as news headlines, tweets, and reviews, short text analysis has gained significant interest in recent times. However, modeling short texts remains a challenging task due to its sparse and noisy nature. In this paper, we propose a new Spherical Correlated Topic Model (SCTM), which takes into account the correlation between topics. Our model integrates word and knowledge graph embeddings to better capture the semantic relationships among short texts. We adopt the von Mises-Fisher distribution to model the high-dimensional word and entity embeddings on a hypersphere, enabling better preservation of the angular relationships between topic vectors. Moreover, knowledge graph embeddings are incorporated to further enrich the semantic meaning of short texts. Experimental results on several datasets demonstrate that our proposed SCTM model outperforms existing models in terms of both topic coherence and document classification. In addition, our model is capable of providing interpretable topics and revealing meaningful correlations among short texts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".