Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets
Bibliographic record
Abstract
Construction of knowledge repositories from web corpora by harvesting linguistic patterns is of benefit for many natural language-processing applications that rely on question-answering schemes. These methods require minimal or no human intervention and can recursively learn new relational facts-instances in a fully automated and scalable manner. This paper explores the performance of tolerance rough set-based learner with respect to two important issues: scalability and its effect on concept drift, by (1) designing a new version of the semi-supervised tolerance rough set-based pattern learner (TPL 2.0), (2) adapting a tolerance form of rough set methodology to categorize linguistic patterns, and (3) extracting categorical information from a large noisy dataset of crawled web pages. This work demonstrates that the TPL 2.0 learner is promising in terms of precision@30 metric when compared with three benchmark algorithms: Tolerant Pattern Learner 1.0, Fuzzy-Rough Set Pattern Learner, and Coupled Bayesian Sets-based learner.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".