Unsupervised Learning for NLP

Описание: Unsupervised learning in Natural Language Processing focuses on discovering hidden patterns and structures within unlabelled text data. Unlike supervised methods, it doesn’t rely on predefined outputs but instead groups similar words, phrases, or documents based on their relationships. Techniques like clustering, topic modeling, and word embeddings help the model uncover meaningful insights such as themes, contexts, and semantic similarities—making it a powerful tool for exploring large volumes of text without manual labeling.