@inproceedings{subramanian-etal-2018-hierarchical,
title = "Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis",
author = "Subramanian, Shivashankar and
Cohn, Trevor and
Baldwin, Timothy",
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/N18-1178",
doi = "10.18653/v1/N18-1178",
pages = "1964--1974",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis
%A Subramanian, Shivashankar
%A Cohn, Trevor
%A Baldwin, Timothy
%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 subramanian-etal-2018-hierarchical
%X 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.
%R 10.18653/v1/N18-1178
%U https://aclanthology.org/N18-1178
%U https://doi.org/10.18653/v1/N18-1178
%P 1964-1974
Markdown (Informal)
[Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis](https://aclanthology.org/N18-1178) (Subramanian et al., NAACL 2018)
ACL
- Shivashankar Subramanian, Trevor Cohn, and Timothy Baldwin. 2018. Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1964–1974, New Orleans, Louisiana. Association for Computational Linguistics.