Distributional approaches in linguistics rely on the idea that words with similar meanings appear in similar contexts. These methods analyze the distribution of words in texts to uncover semantic relationships. Through statistical models, these approaches map the associations between words based on their usage patterns. By examining how words cluster together in various contexts, researchers can gain insights into the hidden structures of language. This allows for more accurate semantic representations and enhances natural language processing tasks. Distributional approaches offer a powerful framework for understanding language and have revolutionized the field of computational linguistics. These techniques continue to drive advancements in machine learning and artificial intelligence.