Conference Proceedings

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

Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin

NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference | Association for Computational Linguistics | Published : 2018

Abstract

Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's finegrained 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 granula..

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