@inproceedings{lam-etal-2018-power,
title = "Power Networks: A Novel Neural Architecture to Predict Power Relations",
author = "Lam, Michelle and
Xu, Catherina and
Prabhakaran, Vinodkumar",
editor = "Alex, Beatrice and
Degaetano-Ortlieb, Stefania and
Feldman, Anna and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4511/",
pages = "97--102",
abstract = "Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the performance of automatically predicting power relations using NLP analysis of social interactions remains wanting. In this paper, we present a novel neural architecture that captures manifestations of power within individual emails which are then aggregated in an order-preserving way in order to infer the direction of power between pairs of participants in an email thread. We obtain an accuracy of 80.4\%, a 10.1\% improvement over state-of-the-art methods, in this task. We further apply our model to the task of predicting power relations between individuals based on the entire set of messages exchanged between them; here also, our model significantly outperforms the 70.0\% accuracy using prior state-of-the-art techniques, obtaining an accuracy of 83.0\%."
}
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<abstract>Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the performance of automatically predicting power relations using NLP analysis of social interactions remains wanting. In this paper, we present a novel neural architecture that captures manifestations of power within individual emails which are then aggregated in an order-preserving way in order to infer the direction of power between pairs of participants in an email thread. We obtain an accuracy of 80.4%, a 10.1% improvement over state-of-the-art methods, in this task. We further apply our model to the task of predicting power relations between individuals based on the entire set of messages exchanged between them; here also, our model significantly outperforms the 70.0% accuracy using prior state-of-the-art techniques, obtaining an accuracy of 83.0%.</abstract>
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%0 Conference Proceedings
%T Power Networks: A Novel Neural Architecture to Predict Power Relations
%A Lam, Michelle
%A Xu, Catherina
%A Prabhakaran, Vinodkumar
%Y Alex, Beatrice
%Y Degaetano-Ortlieb, Stefania
%Y Feldman, Anna
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F lam-etal-2018-power
%X Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the performance of automatically predicting power relations using NLP analysis of social interactions remains wanting. In this paper, we present a novel neural architecture that captures manifestations of power within individual emails which are then aggregated in an order-preserving way in order to infer the direction of power between pairs of participants in an email thread. We obtain an accuracy of 80.4%, a 10.1% improvement over state-of-the-art methods, in this task. We further apply our model to the task of predicting power relations between individuals based on the entire set of messages exchanged between them; here also, our model significantly outperforms the 70.0% accuracy using prior state-of-the-art techniques, obtaining an accuracy of 83.0%.
%U https://aclanthology.org/W18-4511/
%P 97-102
Markdown (Informal)
[Power Networks: A Novel Neural Architecture to Predict Power Relations](https://aclanthology.org/W18-4511/) (Lam et al., LaTeCH 2018)
ACL
- Michelle Lam, Catherina Xu, and Vinodkumar Prabhakaran. 2018. Power Networks: A Novel Neural Architecture to Predict Power Relations. In Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 97–102, Santa Fe, New Mexico. Association for Computational Linguistics.