@inproceedings{vilares-he-2017-detecting,
title = "Detecting Perspectives in Political Debates",
author = "Vilares, David and
He, Yulan",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1165",
doi = "10.18653/v1/D17-1165",
pages = "1573--1582",
abstract = "We explore how to detect people{'}s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.",
}
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<abstract>We explore how to detect people’s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.</abstract>
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%0 Conference Proceedings
%T Detecting Perspectives in Political Debates
%A Vilares, David
%A He, Yulan
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 sep
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F vilares-he-2017-detecting
%X We explore how to detect people’s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.
%R 10.18653/v1/D17-1165
%U https://aclanthology.org/D17-1165
%U https://doi.org/10.18653/v1/D17-1165
%P 1573-1582
Markdown (Informal)
[Detecting Perspectives in Political Debates](https://aclanthology.org/D17-1165) (Vilares & He, EMNLP 2017)
ACL
- David Vilares and Yulan He. 2017. Detecting Perspectives in Political Debates. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1573–1582, Copenhagen, Denmark. Association for Computational Linguistics.