AAAI, Proceedings of Florida Artificial Intelligence Research Society Conference, North America, may. 2017. Available at: <https: />. Date accessed: 24 May. 2017. 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 speaker’s 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.

1. Tracing Linguistic Relations in Winning and
Losing Sides of Explicit Opposing Groups
Ceyda Sanli
ceyda@ntu.edu.sg
23 May 2017, FLAIRS-30, Marco Island,USA
Artificial Intelligence for Big Social Data Analysis
(in collaboration with A. Mondal and E. Cambria)
@NTU Corporate Lab, Singapore
https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15471

2. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island 1
Motivation
Noam Chomsky on grammar rules: “The human mind easily knows
and applies by birth, but hardly formulates to understand the
underlying structure.”
I.
Language
II.
Social
Groups

3. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island 2
Main Result
I.
Language
II.
Social
Groups

4. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island 3
Court Conversations

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Control Parameters
• : linguistic relations
I.
Language
II.
Social
Groups
• : lawyers,
petitioner, respondent
• : Justices,
• : relative power,
• : supportive,
• : unsupported,
• : goal in lawsuits.
agentsinteractions
• time,
• information,
emotions,
• common-sense
knowledge

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Social Exchange Theory*
* Willer, “Network Exchange Theory”, Praeger Press (1999).
Thye et al., “From Status to Power”, Social Forces (2006).
• in the presence of social power groups and
their influence on communication,
• speech adaptation from low power to high
power groups is observed.
linguistic coordination
relative power

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Linguistic Coordination*
* Danescu-Niculescu-Mizil
et al., “Echoes of Power”,
WWW’12.

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Data Set* and Corpus**
• Trialogue discussions of the United States
Supreme Court.
• Coherent speeches in terms of well-organized text
data.
• 50,389 utterances of Justices and lawyers in 195
lawsuits.
• Dynamics is imposed by social roles and a final
winner.
• Strong linguistic coordination is observed because
of both cooperation and competition.
* Danescu-Niculescu-Mizil et al., “Echoes of Power”, WWW’12.
** The Spaeth USA Supreme Court Database. https://scdb.wustl.edu/.
** The USA Supreme Court Transcripts. 2004-2007
https://www.supremecourt.gov/

9. • : linguistic relations
• : linguistic relations
C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island 8
Conversational Groups

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Relation Extraction Algorithm
Recall = 59.92%
Precision = 67.2%
F1 = 63.35%
Pantel et al.
(2004)
Pennacchiotti &
Pantel (2006)
IsA: Hearst
(1992)
PartOf: Girju et
al. (2003)
Klaussner &
Zhekova (2011)
Pantel et al.
(2004)
Scores ~
38.7%-69%

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Illustrations of Patterns

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Linguistic Relations
11
• multiple relations are allowed if the patterns
match with two noun phrases.

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Linking Language and Social Groups
• Pointwise mutual information MI is a metric to
measure coincidence of two discrete random
events.
• To quantify what extend linguistic relations
are addressed by conversation groups :

14. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island
Main Result
13
: conversation
groups

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Overall Messages
14
• Language and social groups are directly
interacting with each other:
Linguistic coordination induced by relative
power in groups.
• Court conversations provide an excellent pool
to investigate trialogue discussions and the
concerned interaction.
• Linguistic relations prove hidden differences
in dynamics of the communication for
distinct power groups.

16. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island
Discussion
15
• Social exchange theory:
only considers microscopic interactions among
agents and doesn’t count the final goal of the
agents, e.g. win and lose of lawsuits.
Here, we create a basis to investigate:
social relative power & linguistic coordination
? ?

17. C Sanli, Tracing Linguistic Relations FLAIRS, AI4BigData, Marco Island
Acknowledgement
16
• Special thanks to San Linn.
• We thank Rolls-Royce@NTU Corporate Lab,
Data Analytics & Complex Systems (DACS):
DACS