@inproceedings{torsi-morante-2018-annotating,
title = "Annotating Claims in the Vaccination Debate",
author = "Torsi, Benedetta and
Morante, Roser",
editor = "Slonim, Noam and
Aharonov, Ranit",
booktitle = "Proceedings of the 5th Workshop on Argument Mining",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5207",
doi = "10.18653/v1/W18-5207",
pages = "47--56",
abstract = "In this paper we present annotation experiments with three different annotation schemes for the identification of argument components in texts related to the vaccination debate. Identifying claims about vaccinations made by participants in the debate is of great societal interest, as the decision to vaccinate or not has impact in public health and safety. Since most corpora that have been annotated with argumentation information contain texts that belong to a specific genre and have a well defined argumentation structure, we needed to adjust the annotation schemes to our corpus, which contains heterogeneous texts from the Web. We started with a complex annotation scheme that had to be simplified due to low IAA. In our final experiment, which focused on annotating claims, annotators reached 57.3{\%} IAA.",
}
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%0 Conference Proceedings
%T Annotating Claims in the Vaccination Debate
%A Torsi, Benedetta
%A Morante, Roser
%Y Slonim, Noam
%Y Aharonov, Ranit
%S Proceedings of the 5th Workshop on Argument Mining
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F torsi-morante-2018-annotating
%X In this paper we present annotation experiments with three different annotation schemes for the identification of argument components in texts related to the vaccination debate. Identifying claims about vaccinations made by participants in the debate is of great societal interest, as the decision to vaccinate or not has impact in public health and safety. Since most corpora that have been annotated with argumentation information contain texts that belong to a specific genre and have a well defined argumentation structure, we needed to adjust the annotation schemes to our corpus, which contains heterogeneous texts from the Web. We started with a complex annotation scheme that had to be simplified due to low IAA. In our final experiment, which focused on annotating claims, annotators reached 57.3% IAA.
%R 10.18653/v1/W18-5207
%U https://aclanthology.org/W18-5207
%U https://doi.org/10.18653/v1/W18-5207
%P 47-56
Markdown (Informal)
[Annotating Claims in the Vaccination Debate](https://aclanthology.org/W18-5207) (Torsi & Morante, ArgMining 2018)
ACL
- Benedetta Torsi and Roser Morante. 2018. Annotating Claims in the Vaccination Debate. In Proceedings of the 5th Workshop on Argument Mining, pages 47–56, Brussels, Belgium. Association for Computational Linguistics.