Enhancing Text Annotation with Few-shot and Active Learning: A Comprehensive Study and Tool Development
Bibliographic record
Abstract
The exponential growth of digital communication channels such as social media and messaging platforms has resulted in an unprecedented influx of unstructured text data, thereby underscoring the need for Natural Language Processing (NLP) techniques. NLP-based techniques play a pivotal role in the analysis and comprehension of human language, facilitating the processing of unstructured text data, and allowing tasks like sentiment analysis, entity recognition, and text classification. NLP-driven applications are made possible due to the advancements in deep learning models. However, deep learning models require a large amount of labeled data for training, thereby making labeled data an indispensable component of these models. Retrieving labeled data can be a major challenge as the task of annotating large amounts of data is laborious and error-prone. Often, professional experts are hired for task-specific data annotation, which can be prohibitively expensive and time-consuming. Moreover, the annotation process can be subjective and lead to inconsistencies, resulting in models that are biased and less accurate. \n \nThis thesis presents a comprehensive study of few-shot and active learning strategies, systems that combine the two techniques, and current text annotation tools while proposing a solution that addresses the aforementioned challenges through the integration of these methods. The proposed solution is an efficient text annotation platform that leverages Few-shot and Active Learning techniques. It has the potential to assist the field of text annotation by enabling organizations to process vast amounts of unstructured text data efficiently. Also, this research paves the way for inspiring ideas and promising growth opportunities in the future of this field.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".