Ideology and Power Identification in Parliamentary Debates
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Jiremalm, Johan
Palmqvist, Oscar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Political debates are vital in shaping public opinion and influencing policy decisions.
However, understanding the complex linguistic structures used by politicians to ascertain
their orientations and power dynamics can be challenging. In this paper
we explore Natural Language Processing techniques for identifying political orientation
and power structures in parliamentary debates. We introduce a Located
Missing Labels-loss in order to train jointly to predict both power and ideology.
Furthermore, our proposed method also trains to predict a third synthetically generated
polarity label. Finally, we combine this training method with pre-processing
steps including back-translation and meta data inclusion. Our results show that our
method manages to improve upon conventional methods of fine-tuning. We take
part in the Touché competition as part of CLEF 2024 and find that our method
achieves the highest performance out of all participants [1].
Keywords: Political
Beskrivning
Ämne/nyckelord
Political Classification , NLP , LLM , MLML