With argumentation trails we introduce an approach of finding relevant associations between arbitrary terms. An argumentation trail between two terms is an ordered list of cooccurrences, providing a connected path from the origin to the endpoint of the argumentation. Within this paper the automatic generation of argumentation trails is examined and assessed. Furthermore, the formal representation of these trails as Topic Maps is implemented. This enables the integration of argumentation trails with further background information to support sensemaking or other discourse enriching techniques for academic or political debates.

1. Automatic Extraction of Topic Maps based Argumentation Trails Text Mining Services Conference Leipzig, 2009/03/25 Marco Büchler, Lutz Maicher, Frederik Baumgardt, Benjamin Bock Natural Language Processing Group Department of Computer Science University of Leipzig

2. Starting Point: Panionion ‏

4. Technical details

5. Text source

8. Methodology

9. Topic Maps ‏

10. Data model of Topic Maps (Topics) Nikolaikirche variant St. Nicholas Church St. Nikolai name English scope 1165 occurrence www.nikolaikirche -leipzig.de/ occurrence foundation type website type

11. Data model of Topic Maps (Associations) St. Nikolai Leipzig association container-containee ass. role role player container containee role type

14. Extraction of typed significant terms Corpus is categorized in several classification schemas. Split corpus into several sub corpora Medusa age gender geography .... Categorized co-occurrences/terms Tomcat/ Prefuse Age gender geography (Source:Taken from bachelor thesis slides of Marcus Puchalla.) ‏

15. Results

16. Several graph properties

17. Visualisation of two argumentation trails

18. Marco Büchler onotoa.topicmapslab.de Topic-Maps-Ontologie for the Argumentation Trails Topic Maps and Argumentation Trails

23. - Reduction of graph comlexity - e. g. by semantic pre-clustering or - authors restrictions - Weighting of argumentation trails - e. g. Trails containing hubs should be weighted lower - Improvements in visualisation - Clustering of similar trails to a bunch of semanitic similar trails - Improvements in typing nodes and especially edges Further work / conclusion