Unlabeled Corpus

Unlabeled corpora, vast collections of text without human-assigned labels, are increasingly central to natural language processing (NLP) research. Current efforts focus on leveraging these resources to improve model performance in various tasks, including text classification, named entity recognition, and even generating training data for supervised learning, often employing techniques like language modeling, hierarchical clustering, and dense retrieval to extract valuable information. This research is significant because it addresses the limitations of relying solely on expensive labeled datasets, enabling the development of more data-efficient and robust NLP systems applicable to diverse domains and low-resource languages. The resulting models and techniques are impacting fields ranging from legal case summarization to emotion intensity prediction.