@inproceedings{lehmann-derczynski-2019-political,
title = "Political Stance in {D}anish",
author = "Lehmann, Rasmus and
Derczynski, Leon",
editor = "Hartmann, Mareike and
Plank, Barbara",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6121",
pages = "197--207",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lehmann-derczynski-2019-political">
<titleInfo>
<title>Political Stance in Danish</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rasmus</namePart>
<namePart type="family">Lehmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leon</namePart>
<namePart type="family">Derczynski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-sep–oct</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mareike</namePart>
<namePart type="family">Hartmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="family">Plank</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Linköping University Electronic Press</publisher>
<place>
<placeTerm type="text">Turku, Finland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">lehmann-derczynski-2019-political</identifier>
<location>
<url>https://aclanthology.org/W19-6121</url>
</location>
<part>
<date>2019-sep–oct</date>
<extent unit="page">
<start>197</start>
<end>207</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Political Stance in Danish
%A Lehmann, Rasmus
%A Derczynski, Leon
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F lehmann-derczynski-2019-political
%X 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.
%U https://aclanthology.org/W19-6121
%P 197-207
Markdown (Informal)
[Political Stance in Danish](https://aclanthology.org/W19-6121) (Lehmann & Derczynski, NoDaLiDa 2019)
ACL
- Rasmus Lehmann and Leon Derczynski. 2019. Political Stance in Danish. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 197–207, Turku, Finland. Linköping University Electronic Press.