@inproceedings{52cf5fd6d57e499bbca685defa700e30,
title = "Political Stance in Danish",
abstract = "The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, and present annotation guidelines to allow further expansion of the generated dataset. Furthermore, three models based on an LSTM architecture are designed, implemented and optimized to perform the task of stance detection for the generated dataset. Experiments are performed using conditionality and bi-directionality for these models, and using either singular word embeddings or averaged word embeddings for an entire quote, to determine the optimal model design. The simplest model design, applying neither conditionality or bi-directionality, and averaged word embeddings across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quotes politician, and the party affiliation of the quoted politician, greatly improved performance of the strongest model.",
keywords = "Stance Detection, Dataset Generation, LSTM Models, Conditionality, Bi-directionality, Word Embeddings, Annotation Guidelines, Political Quotes, Danish Politicians, Model Optimization, Stance Detection, Dataset Generation, LSTM Models, Conditionality, Bi-directionality, Word Embeddings, Annotation Guidelines, Political Quotes, Danish Politicians, Model Optimization",
author = "Rasmus Lehmann and Leon Derczynski",
year = "2019",
language = "English",
series = "NEALT (Northern European Association of Language Technology) Proceedings Series",
pages = "197–207",
booktitle = "Proceedings of the Nordic Conference of Computational Linguistics (2019)",
publisher = "Link{\"o}ping University Press",
}