This paper presents our efforts to create argument structures from meeting transcripts automatically. We show that unit labels of argument diagrams can be learnt and predicted by a computer with an accuracy of 78,52% and 51,43% on an unbalanced and balanced set respectively. We used a corpus of over 250 argument diagrams that was manually created by applying the Twente Argument Schema. In
this paper we also elaborate on this schema and we discuss applications and the role we foresee the diagrams to play.
|Name||Frontiers in Artificial Intelligence and Applications|
|Conference||in 1st International Conference on Computational Models of Argument, Liverpool, UK.|
|Period||1/09/06 → …|
- HMI-MI: MULTIMODAL INTERACTIONS
- EC Grant Agreement nr.: FP6/506811