We describe initial work on extensions to word highlighting for multiparticipant chat to aid users in finding messages of interest, especially during times of high traffic in chat rooms. We have annotated a corpus of chat messages from a technical chat domain (Ubuntu’s technical support), indicating whether they are related to Ubuntu’s new desktop environment Unity. We also created an unsupervised learning algorithm, in which relations are represented with a graph, and applied this to find words related to Unity so they can be highlighted in new, unseen chat messages. On the task of finding relevant messages, our approach outperformed two baseline approaches that are similar to current state-of-the-art word highlighting methods in chat clients.

There have been significant advances in developing each phase of the mission. Secondly, based on our everyday algorithms that allow researchers to examine these experience with physical objects in our environment, behaviors in simulation (Luke et al. 2005), generally assuming the three major physical states of matter, solid, liquid and noise-free estimates of the agents' own, neighbors' and gas, represent a natural and intuitive means of describing the targets' positions. However, the actual information flow into types of motions a swarm of mobile robots can perform as biological agents' in terms of the sensing, processing and they cluster, transit or wander (Gage 1992).