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Machine learning for meeting analysis

Publication ,  Conference
Kim, B; Rudin, C
Published in: AAAI Workshop - Technical Report
January 1, 2013

Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: i) there are common macro-patterns in the way social dialogue acts are interspersed throughout a meeting, and ii) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Duke Scholars

Published In

AAAI Workshop - Technical Report

ISBN

9781577356288

Publication Date

January 1, 2013

Volume

WS-13-17

Start / End Page

59 / 61
 

Citation

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Chicago
ICMJE
MLA
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Kim, B., & Rudin, C. (2013). Machine learning for meeting analysis. In AAAI Workshop - Technical Report (Vol. WS-13-17, pp. 59–61).
Kim, B., and C. Rudin. “Machine learning for meeting analysis.” In AAAI Workshop - Technical Report, WS-13-17:59–61, 2013.
Kim B, Rudin C. Machine learning for meeting analysis. In: AAAI Workshop - Technical Report. 2013. p. 59–61.
Kim, B., and C. Rudin. “Machine learning for meeting analysis.” AAAI Workshop - Technical Report, vol. WS-13-17, 2013, pp. 59–61.
Kim B, Rudin C. Machine learning for meeting analysis. AAAI Workshop - Technical Report. 2013. p. 59–61.

Published In

AAAI Workshop - Technical Report

ISBN

9781577356288

Publication Date

January 1, 2013

Volume

WS-13-17

Start / End Page

59 / 61