C4.5: programs for machine learning
C4.5: programs for machine learning
Designing argumentation tools for collaborative learning
Visualizing argumentation
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
A shallow model of backchannel continuers in spoken dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Browsing recorded meetings with ferret
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Verbal behavior of the more and the less influential meeting participant
Proceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
Modelling and detecting decisions in multi-party dialogue
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Real-time decision detection in multi-party dialogue
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
The CALO meeting assistant system
IEEE Transactions on Audio, Speech, and Language Processing
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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.