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Learning to predict pitch accents and prosodic boundaries in Dutch

Authors:

Martin Reynaert,

Antal van den Bosch,

Walter Daelemans,

Véronique HosteAuthors Info & Claims

ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1

Pages 489 - 496

https://doi.org/10.3115/1075096.1075158

Published: 07 July 2003 Publication History

Abstract

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shallow, easy-to-compute features. We train the algorithms on both tasks individually and on the two tasks simultaneously. The parameters of both algorithms and the selection of features are optimized per task with iterative deepening, an efficient wrapper procedure that uses progressive sampling of training data. Results show a consistent significant advantage of MBL over CART, and also indicate that task combination can be done at the cost of little generalization score loss. Tests on cross-validated data and on held-out data yield F-scores of MBL on accent placement of 84 and 87, respectively, and on breaks of 88 and 91, respectively. Accent placement is shown to outperform an informed baseline rule; reliably predicting breaks other than those already indicated by intra-sentential punctuation, however, appears to be more challenging.

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Cited By

Schweitzer KWalsh MMöbius BRiester ASchweitzer ASchütze HLascarides AGardent CNivre J(2009)Frequency mattersProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics10.5555/1609067.1609148(728-736)Online publication date: 30-Mar-2009
https://dl.acm.org/doi/10.5555/1609067.1609148
Schmid HAtterer M(2004)New statistical methods for phrase break predictionProceedings of the 20th international conference on Computational Linguistics10.3115/1220355.1220450(659-es)Online publication date: 23-Aug-2004
https://dl.acm.org/doi/10.3115/1220355.1220450
Gabsdil MLemon OScott D(2004)Combining acoustic and pragmatic features to predict recognition performance in spoken dialogue systemsProceedings of the 42nd Annual Meeting on Association for Computational Linguistics10.3115/1218955.1218999(343-es)Online publication date: 21-Jul-2004
https://dl.acm.org/doi/10.3115/1218955.1218999

Learning to predict pitch accents and prosodic boundaries in Dutch
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image DL Hosted proceedings

ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1

July 2003

571 pages

Program Chairs:
Erhard W. Hinrichs,
Dan Roth

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Association for Computational Linguistics

United States

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Published: 07 July 2003

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Cited By

Schweitzer KWalsh MMöbius BRiester ASchweitzer ASchütze HLascarides AGardent CNivre J(2009)Frequency mattersProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics10.5555/1609067.1609148(728-736)Online publication date: 30-Mar-2009
https://dl.acm.org/doi/10.5555/1609067.1609148
Schmid HAtterer M(2004)New statistical methods for phrase break predictionProceedings of the 20th international conference on Computational Linguistics10.3115/1220355.1220450(659-es)Online publication date: 23-Aug-2004
https://dl.acm.org/doi/10.3115/1220355.1220450
Gabsdil MLemon OScott D(2004)Combining acoustic and pragmatic features to predict recognition performance in spoken dialogue systemsProceedings of the 42nd Annual Meeting on Association for Computational Linguistics10.3115/1218955.1218999(343-es)Online publication date: 21-Jul-2004
https://dl.acm.org/doi/10.3115/1218955.1218999

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