C4.5: programs for machine learning
C4.5: programs for machine learning
Information Retrieval
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Latent semantic analysis for dialogue act classification
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Dialogue act recognition with Bayesian networks for Dutch dialogues
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
Sentence Similarity Based on Semantic Nets and Corpus Statistics
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Investigating the portability of corpus-derived cue phrases for dialogue act classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A machine learning approach to speech act classification using function words
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
A multi-classifier approach to dialogue act classification using function words
Transactions on Computational Collective Intelligence VII
Transactions on Computational Collective Intelligence IX
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This paper extends a novel technique for the classification of short texts as Dialogue Acts, based on structural information contained in function words. It investigates the new challenge of discriminating between instructions and a non-instruction mix of questions and statements. The proposed technique extracts features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. Consequently this is a potentially challenging task for the function-word based approach as the salient feature of an instruction is an imperative verb, which will always be replaced by a wildcard. Nevertheless, the results of the decision tree classifiers produced provide evidence for potentially highly effective classification and they are comparable with initial work on question classification. Improved classification accuracy is expected in future through optimisation of feature extraction.