@inproceedings{prabhakaran-etal-2018-author,
title = "Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions",
author = "Prabhakaran, Vinodkumar and
Ganeshkumar, Premkumar and
Rambow, Owen",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1096",
doi = "10.18653/v1/N18-1096",
pages = "1057--1068",
abstract = "Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do {---} an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.",
}
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%0 Conference Proceedings
%T Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions
%A Prabhakaran, Vinodkumar
%A Ganeshkumar, Premkumar
%A Rambow, Owen
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F prabhakaran-etal-2018-author
%X Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do — an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.
%R 10.18653/v1/N18-1096
%U https://aclanthology.org/N18-1096
%U https://doi.org/10.18653/v1/N18-1096
%P 1057-1068
Markdown (Informal)
[Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions](https://aclanthology.org/N18-1096) (Prabhakaran et al., NAACL 2018)
ACL