Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82564
Title: Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups
Authors: Sanli, Ceyda
Mondal, Anupam
Cambria, Erik
Keywords: Artificial Intelligence
Computation and Language
Issue Date: 2017
Source: Sanli, C., Mondal, A., & Cambria, E. (2017). Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups. Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference.
Abstract: Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, reshape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speakers reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations hidden in a conversation with explicitly stated winners and losers. We examine open access data of the United States Supreme Court. Our understanding is crucial in big data research to guide how transition states in opinion mining and decision-making should be modeled and how this required knowledge to guide the model should be pinpointed, by filtering large amount of data.
URI: https://hdl.handle.net/10356/82564
http://hdl.handle.net/10220/42776
Rights: © 2017 Association for the Advancement of Artificial Intelligence (AAAI). This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, Association for the Advancement of Artificial Intelligence (AAAI). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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