Semi-supervised and unsupervised categorization of posts in Web discussion forums using part-of-speech information and minimal features
Bibliographic record
Abstract
Web discussion forums typically contain posts that fall into different categories such as question, solution, feedback, spam, etc. Automatic identification of these categories can aid information retrieval that is tailored for specific user requirements.Previously, a number of supervised methods have attempted to solve this problem; however, these depend on the availability of abundant training data.A few existing unsupervised and semi-supervised approaches are either focused on identifying only one or two categories, or do not discuss category-specific performance.In contrast, this work proposes methods for identifying multiple categories, and also analyzes the category-specific performance.These methods are based on sequence models (specifically, hidden Markov Models) that can model language for each category using both probabilistic word and part-of-speech information, and minimal manually specified features.The unsupervised version initializes the models using clustering, whereas the semi-supervised version uses few manually labeled forum posts.Empirical evaluations demonstrate that these methods are more accurate than previous ones.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".