# Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis

arXiv: https://arxiv.org/abs/1805.02823 · HTML: https://mlsanity.com/paper/1805.02823/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 5 of 5)

- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [Changes in Policy Preferences in German Tweets during the COVID Pandemic](https://mlsanity.com/paper/2308.04444/) (2023)
- [Actor Identification in Discourse: A Challenge for LLMs?](https://mlsanity.com/paper/2402.00620/) (2024)
- [Knowledge Graph Representation for Political Information Sources](https://mlsanity.com/paper/2404.03437/) (2024)
- [Strategies for political-statement segmentation and labelling in unstructured text](https://mlsanity.com/paper/2503.07179/) (2025)

## Abstract (arXiv)

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.