Latent Beta-Liouville Probabilistic Modeling for Bursty Topic Discovery in Textual Data
Bibliographic record
Abstract
Topic modeling has become a fundamental technique for uncovering latent thematic structures within large collections of textual data. However, conventional models often struggle to capture the burstiness of topics. This characteristic, where the occurrence of a word increases its likelihood of subsequent appearances in a document, is fundamental in natural language processing. To address this gap, we introduce a novel topic modeling framework, integrating Beta-Liouville and Dirichlet Compound Multinomial distributions. Our approach, named Beta-Liouville Dirichlet Compound Multinomial Latent Dirichlet Allocation (BLDCMLDA), is designed to specifically model word burstiness and support a wide range of adaptable topic proportion patterns. Through experiments on diverse benchmark text datasets, the BLDCMLDA model has demonstrated superior performance over conventional models. Our promising results in terms of perplexity and coherence scores demonstrate the effectiveness of BLDCMLDA in capturing the nuances of word usage dynamics in natural language.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".