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Computational processing of text exchanged in interactive venues in which participants engage in simultaneous conversations can benefit from techniques for automatically grouping overlapping sequences of messages into separate conversations, a problem known as "disentanglement." While previous methods exploit both lexical and non-lexical information that exists in conversations for this task, the inter-dependency between the meaning of a message and its temporal and social contexts is largely ignored. Our approach exploits contextual properties (both explicit and hidden) to probabilistically expand each message to provide a more accurate message representation. Extensive experimental evaluations show our approach outperforms the best previously known technique.

Context-based message expansion for disentanglement of interleaved text conversations