Detecting Perspectives in Political Debates

David Vilares, Yulan He


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.
Anthology ID:
D17-1165
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1573–1582
Language:
URL:
https://www.aclweb.org/anthology/D17-1165
DOI:
10.18653/v1/D17-1165
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1165.pdf
Video:
 https://vimeo.com/238232359