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<p>Text-based chat systems are widely used within the Department of Defense, but the standard systems available do not provide robust  capabilities for search, information retrieval, or information assurance. The objective of this research is to explore methods for the  extraction of conversation threads from text-based chat systems in order to enable such tasks. As part of the research, we manually  annotated over 20,000 Internet Relay Chat posts with conversation thread information and constructed a probabilistic model for automatically classifying posts according to conversation thread. We also provide an algorithm for extracting these conversation  threads from the chat session in order to form discrete documents that may be used in a vector space model information retrieval  system. We elaborate how this technique can be used to support search and data mining systems, as well as auditing tasks and guard  functions in a security system. Using the developed probabilistic models, we have achieved classification results on par with those of  human annotators.
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<br>Subjects: Linguistic String Parser (Computer grammar); Automatic speech recognition; Text messages (Telephone systems)</div>
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Adams, Paige Holland.</div>
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