  We present a simple unsupervised approach for answer identification in
organizational group chat. In recent years, organizational group chat is on the
rise enabling asynchronous text-based collaboration between co-workers in
different locations and time zones. Finding answers to questions is often
critical for work efficiency. However, group chat is characterized by
intertwined conversations and 'always on' availability, making it hard for
users to pinpoint answers to questions they care about in real-time or search
for answers in retrospective. In addition, structural and lexical
characteristics differ between chat groups, making it hard to find a 'one model
fits all' approach. Our Kernel Density Estimation (KDE) based clustering
approach termed Ans-Chat implicitly learns discussion patterns as a means for
answer identification, thus eliminating the need to channel-specific tagging.
Empirical evaluation shows that this solution outperforms other approached.
