Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis

Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin


Abstract
Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party’s fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left–right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.
Anthology ID:
N18-1178
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1964–1974
Language:
URL:
https://www.aclweb.org/anthology/N18-1178
DOI:
10.18653/v1/N18-1178
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1178.pdf
Video:
 http://vimeo.com/277672945