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Article

A machine learning approach to speech act classification using function words

Authors:

Keeley CrockettAuthors Info & Claims

KES-AMSTA'10: Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II

Pages 82 - 91

Published: 23 June 2010 Publication History

Abstract

This paper presents a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. It focuses on classifying questions or non-questions as a generally useful task in agent-based systems. The proposed technique extracts salient features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. The Decision Tree, which is a well-established classification technique, has been chosen for this work. Experiments provide evidence of potential for highly effective classification, with a significant achievement on a challenging dataset, before any optimisation of feature extraction has taken place.

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Cited By

Sherkawi LGhneim NDakkak O(2018)Arabic Speech Act Recognition TechniquesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/317057617:3(1-12)Online publication date: 13-Feb-2018
https://dl.acm.org/doi/10.1145/3170576
O'shea JBandar ZCrockett K(2014)A new benchmark dataset with production methodology for short text semantic similarity algorithmsACM Transactions on Speech and Language Processing (TSLP)10.1145/253704610:4(1-63)Online publication date: 3-Jan-2014
https://dl.acm.org/doi/10.1145/2537046
O'Shea JBandar ZCrockett K(2012)A multi-classifier approach to dialogue act classification using function wordsTransactions on Computational Collective Intelligence VII10.5555/2363384.2363390(119-143)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.5555/2363384.2363390
Show More Cited By

A machine learning approach to speech act classification using function words
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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Information & Contributors

Information

Published In

cover image Guide Proceedings

KES-AMSTA'10: Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II

June 2010

411 pages

ISBN:3642135404

Editors:
Piotr Jędrzejowicz
Gdynia Maritime University, Gdynia, Poland
,
Ngoc Thanh Nguyen
Wroclaw University of Technology, Wroclaw, Poland
,
Robert J. Howlett
University of Brighton, Brighton, United Kingdom
,
Lakhmi C. Jain
University of South Australia, Mawson Lakes, SA, Australia

Sponsors

Gdynia Maritime University

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 June 2010

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Cited By

Sherkawi LGhneim NDakkak O(2018)Arabic Speech Act Recognition TechniquesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/317057617:3(1-12)Online publication date: 13-Feb-2018
https://dl.acm.org/doi/10.1145/3170576
O'shea JBandar ZCrockett K(2014)A new benchmark dataset with production methodology for short text semantic similarity algorithmsACM Transactions on Speech and Language Processing (TSLP)10.1145/253704610:4(1-63)Online publication date: 3-Jan-2014
https://dl.acm.org/doi/10.1145/2537046
O'Shea JBandar ZCrockett K(2012)A multi-classifier approach to dialogue act classification using function wordsTransactions on Computational Collective Intelligence VII10.5555/2363384.2363390(119-143)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.5555/2363384.2363390
O'Shea JBandar ZCrockett K(2011)Using a slim function word classifier to recognise instruction dialogue actsProceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications10.5555/2023144.2023150(26-34)Online publication date: 29-Jun-2011
https://dl.acm.org/doi/10.5555/2023144.2023150

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