PolText 2016 : The International Conference on the Advances in Computational Analysis of Political Text : proceedings of the conference : sponsored by the European Social Fund, Operational Programme Efficient Human Resources 2014–2020
Seitenbereich:
88-93
Veranstaltungstitel:
International Conference on the Advances in Computational Analysis of Political Text
General political topics, like social security and foreign affairs, recur in electoral
manifestos across countries. The Comparative Manifesto Project collects and manually codes manifestos of political parties
from all around the world, detecting political topics at sentence level. Since manual coding is time-consuming and allows
for annotation inconsistencies, in this work
we present an automated approach to topical coding of political manifestos. We
first train three independent sentence-level
classifiers – one for detecting the topic
and two for detecting topic shifts – and
then globally optimize their predictions using a Markov Logic network. Experimental results show that the proposed global
model achieves high classification performance and significantly outperforms the
local sentence-level topic classifier.
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