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.

本研究解决了传统方法无法在无域适用情况下对政治声明进行分割和标记的限制。我们提出了一系列统一的分割与标记框架，并展示了其在原始文本中实现竞争性准确性的潜力。此外，我们通过应用该方法于英国下议院的记录，追踪了过去三十年来四个主要政党的政治轨迹，显示了该方法的研究价值和影响。 

政治声明在非结构化文本中的分割和标记策略