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      Political Text Scaling Meets Computational Semantics

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          Abstract

          During the last fifteen years, text scaling approaches have become a central element for the text-as-data community. However, they are based on the assumption that latent positions can be captured just by modeling word-frequency information from the different documents under study. We challenge this by presenting a new semantically aware unsupervised scaling algorithm, SemScale, which relies upon distributional representations of the documents under study. We conduct an extensive quantitative analysis over a collection of speeches from the European Parliament in five different languages and from two different legislations, in order to understand whether a) an approach that is aware of semantics would better capture known underlying political dimensions compared to a frequency-based scaling method, b) such positioning correlates in particular with a specific subset of linguistic traits, compared to the use of the entire text, and c) these findings hold across different languages. To support further research on this new branch of text scaling approaches, we release the employed dataset and evaluation setting, an easy-to-use online demo, and a Python implementation of SemScale.

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          Most cited references 11

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          The Stanford CoreNLP Natural Language Processing Toolkit

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            DBpedia: A Nucleus for a Web of Open Data

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              A Review of Relational Machine Learning for Knowledge Graphs

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                Author and article information

                Journal
                12 April 2019
                Article
                1904.06217
                b5b30452-a47e-4e76-91d8-3d7eac275e62

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CL

                Theoretical computer science

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