  Most online message threads inherently will be cluttered and any new user or
an existing user visiting after a hiatus will have a difficult time
understanding whats being discussed in the thread. Similarly cluttered
responses in a message thread makes analyzing the messages a difficult problem.
The need for disentangling the clutter is much higher when the platform where
the discussion is taking place does not provide functions to retrieve reply
relations of the messages. This introduces an interesting problem to which
\cite{wang2011learning} phrases as a structural learning problem. We create
vector embeddings for posts in a thread so that it captures both linguistic and
positional features in relation to a context of where a given message is in.
Using these embeddings for posts we compute a similarity based connectivity
matrix which then converted into a graph. After employing a pruning mechanisms
the resultant graph can be used to discover the reply relation for the posts in
the thread. The process of discovering or disentangling chat is kept as an
unsupervised mechanism. We present our experimental results on a data set
obtained from Telegram with limited meta data.

Computation and Language