期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：688-693
语种：English
出版社：ACL Anthology
摘要：Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.
