Semi-Automatic Labeling of Training Data Sets in Text Classification
Bibliographic record
Abstract
Web includes digital libraries and billions of text documents. A fast and simple search through this sizeable set is important for users and researchers. Since manual or rule based document classification is a difficult, time consuming process, automatic classification systems are absolutely needed. Automatic text classification systems demand extensive and proper training data sets. To provide these data sets, usually, numerous unlabeled documents are labeled manually by experts. Manual labeling of documents is a difficult and time consuming process. Moreover, in manual labeling, due to human exhaustion and carelessness, there is the possibility of mistakes.In this study, semi-automatic creation of training data set has been proposed in a way that only a small percentage of this extensive set’s documents is labeled manually and the remaining percentage is done automatically. Results show that by labeling only ten percent of the training set, remaining documents can be automatically labeled with 98 percent of accuracy. It is worth mentioning that this reduction in accuracy only occurs in standard data sets, while for large practical data sets, this reduction is trivial compared to the accuracy reduction resulted by human exhaustion and carelessness.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".