  In human-computer conversation systems, the context of a user-issued
utterance is particularly important because it provides useful background
information of the conversation. However, it is unwise to track all previous
utterances in the current session as not all of them are equally important. In
this paper, we address the problem of session segmentation. We propose an
embedding-enhanced TextTiling approach, inspired by the observation that
conversation utterances are highly noisy, and that word embeddings provide a
robust way of capturing semantics. Experimental results show that our approach
achieves better performance than the TextTiling, MMD approaches.
