Abstract
The increase in persistent conversations in the form of chat and instant messaging (IM) has presented new opportunities for researchers. This paper describes a method for evaluating and visualizing persistent conversations by creating a speech act profile for conversation participants using speech act theory and concepts from fuzzy logic. This method can be used either to score a participant based on possible intentions or to create a visual map of those intentions. Transcripts from the Switchboard corpus, which have been marked up with speech act labels according to a SWBD-DAMSL tag set of 42 tags, are used to train language models and a modified hidden Markov model (HMM) to obtain probabilities for each speech act type for a given sentence. Rather than choosing the speech act with the maximum probability and assigning it to the sentence, the probabilities are aggregated for each conversation participant creating a set of speech act profiles, which can be visualized as a radar graphs. Several example profiles are shown along with possible interpretations. The profiles can be used as an overall picture of a conversation, and may be useful in various analyses of persistent conversations including information retrieval, deception detection, and online technical support monitoring.
Original language | English |
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Article number | DDPCN06 |
Pages (from-to) | 1713-1722 |
Number of pages | 10 |
Journal | Proceedings of the Hawaii International Conference on System Sciences |
Volume | 37 |
State | Published - 2004 |
Event | Proceedings of the Hawaii International Conference on System Sciences - Big Island, HI., United States Duration: 5 Jan 2004 → 8 Jan 2004 |