This paper presents the results of an annotation study focused on the fine-grained analysis of argumentation structures in scientific publications. Our new annotation scheme specifies four types of binary argumentative relations between sentences, resulting in the representation of arguments as small graph structures. We developed an annotation tool that supports the annotation of such graphs and carried out an annotation study with four annotators on 24 scientific articles from the domain of educational research. For calculating the inter-annotator agreement, we adapted existing measures and developed a novel graph-based agreement measure which reflects the semantic similarity of different annotation graphs.
CITATION STYLE
Kirschner, C., Eckle-Kohler, J., & Gurevych, I. (2015). Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications. In 2nd Workshop on Argumentation Mining, ArgMining 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 1–11). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w15-0501
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