Enhancing Text Annotation with Few-shot and Active Learning: A Comprehensive Study and Tool Development

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. This 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