@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}