Improving Topic Quality with Interactive Beta-Liouville Mixture Allocation Model
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
One of the major tasks in natural language processing is to categorize texts into different categories. Topic models are an important set of tools for categorizing texts and so are mixture models since both models learn patterns from data in an unsupervised manner. The introduction of latent Dirichlet allocation (LDA) triggered a lot of research in this domain. Recent research investigates the use of distributions other than Dirichlet for the topic proportions in LDA especially generalized Dirichlet and Beta-Liouville distributions in addition to adding useful attributes specific to the task at hand. Improving the quality of topics extracted from these models is important for accurate inference and unsupervised language tasks. Owing to this cause, in this paper, we propose interactive Beta-Liouville mixture allocation (iBLMA) model which combines the clustering capabilities of mixture models with interactive learning which helps the user modify the topic weights of irrelevant words within the topic. We show the efficiency of our model with experiments on two different text datasets.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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