Brown clustering

Brown clustering is a hierarchical clustering algorithm used primarily in natural language processing (NLP) to group words or phrases based on their co-occurrence in a text corpus. Developed by Peter Brown and his colleagues in the early 1990s, the method aims to identify clusters of words that share similar contexts, thereby capturing a form of semantic similarity. ### Key Concepts: 1. **Co-occurrence**: The method evaluates how often words appear together in the same contexts (e.g.