A dataset for multi-target stance detection

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TypeArticle
Proceedings titleProceedings of Conference of the European Chapter of the Association for Computational Linguistics. Volume 2
Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, April 3-7, 2017, Valencia, Spain
ISBN978-151083860-4
Pages551557
Subjectcomputational linguistics; linguistics; current models; joint learning; multi-targets; neural models; classification (of information)
AbstractCurrent models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification. © 2017 Association for Computational Linguistics.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23002559
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Record identifier2b4e294d-e3ed-4e09-b947-625f42349563
Record created2017-11-30
Record modified2017-11-30
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