# Strategies for political-statement segmentation and labelling in unstructured text

arXiv: https://arxiv.org/abs/2503.07179 · HTML: https://mlsanity.com/paper/2503.07179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation](https://mlsanity.com/paper/2406.16678/) (2024)
- [Sentiment and position-taking analysis of parliamentary debates: A systematic literature review](https://mlsanity.com/paper/1907.04126/) (2019)
- [Political Text Scaling Meets Computational Semantics](https://mlsanity.com/paper/1904.06217/) (2019)
- [MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset](https://mlsanity.com/paper/2305.01211/) (2023)
- [Constraining Linear-chain CRFs to Regular Languages](https://mlsanity.com/paper/2106.07306/) (2021)
- [Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis](https://mlsanity.com/paper/1805.02823/) (2018)
- [Additive manifesto decomposition: A policy domain aware method for understanding party positioning](https://mlsanity.com/paper/2305.10136/) (2023)
- [Digitization of the Australian Parliamentary Debates, 1998-2022](https://mlsanity.com/paper/2304.04561/) (2023)

## Abstract (arXiv)

Analysis of parliamentary speeches and political-party manifestos has become an integral area of computational study of political texts. While speeches have been overwhelmingly analysed using unsupervised methods, a large corpus of manifestos with by-statement political-stance labels has been created by the participants of the MARPOR project. It has been recently shown that these labels can be predicted by a neural model; however, the current approach relies on provided statement boundaries, limiting out-of-domain applicability. In this work, we propose and test a range of unified split-and-label frameworks -- based on linear-chain CRFs, fine-tuned text-to-text models, and the combination of in-context learning with constrained decoding -- that can be used to jointly segment and classify statements from raw textual data. We show that our approaches achieve competitive accuracy when applied to raw text of political manifestos, and then demonstrate the research potential of our method by applying it to the records of the UK House of Commons and tracing the political trajectories of four major parties in the last three decades.