Individual words are not powerful enough for many complex language classification problems. N-gram features include word context information, but are limited to contiguous word sequences. In this paper, we propose to use phrase patterns to extend n-grams for analyzing conversations, using a discriminative approach to learning patterns with a combination of words and word classes to address data sparsity issues. Improvements in performance are reported for two conversation analysis tasks: speaker role recognition and alignment classification.