  We investigate the task of inferring conversational dependencies between
messages in one-on-one online chat, which has become one of the most popular
forms of customer service. We propose a novel probabilistic classifier that
leverages conversational, lexical and semantic information. The approach is
evaluated empirically on a set of customer service chat logs from a Chinese
e-commerce website. It outperforms heuristic baselines.
