Towards Improving Text Classification Tasks Based on Knowledge Graphs for Limited Labeled Data
Bibliographic record
Abstract
Pre-trained Transformer models have become popular in various Natural Language Processing (NLP) tasks, following a two-step process of ’pre-training’ and ’fine-tuning’. However, with the abundance of information on the web, some domain knowledge may be lacking. This can result in poor performance during the fine-tuning step when there is limited training data available. To address this issue in the case of limited data, we propose a knowledge graph-based data expansion method that enables the model to achieve good results even when there is limited data in the fine-tuning step. We extract entities in the text through Named Entity Recognition and then search for related information in the knowledge graph to expand the text’s content. This allows the pretrained model to acquire more external knowledge and enhance its training. We used our data expansion method to conduct experiments on the following well-known models, i.e., BERT, RoBERTa, and GPT-3. Our experiments show that our approach can improve the accuracy of language models on text classification tasks when training data is limited.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".