Exploring Features and Classifiers for Dialogue Act Segmentation

Authors:
Harm Akker;Christian Schulz
Affiliations:
Twente University, Enschede, The Netherlands;Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI), Saarbrücken, Germany
Venue:
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Year:
2008

Citing 4
Cited 1

Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II
Hybrid Multi-step Disfluency Detection

MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
A support vector machine approach to dutch part-of-speech tagging

IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Toward joint segmentation and classification of dialog acts in multiparty meetings

MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction

Classification of feedback expressions in multimodal data

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper takes a classical machine learning approach to the task of Dialogue Act segmentation. A thorough empirical evaluation of features, both used in other studies as well as new ones, is performed. An explorative study to the effectiveness of different classification methods is done by looking at 29 different classifiers implemented in WEKA. The output of the developed classifier is examined closely and points of possible improvement are given.