Dialogue-act tagging using smart feature selection: results on multiple corpora

Daan Verbree; R.J. Rienks; Dirk K.J. Heylen

doi:10.1109/SLT.2006.326819

Dialogue-act tagging using smart feature selection: results on multiple corpora

Daan Verbree, R.J. Rienks, Dirk K.J. Heylen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

31 Citations (Scopus)

278 Downloads (Pure)

Abstract

This paper presents an overview of our on-going work on dialogueact classification. Results are presented on the ICSI, Switchboard, and on a selection of the AMI corpus, setting a baseline for forthcoming research. For these corpora the best accuracy scores obtained are 89.27%, 65.68% and 59.76%, respectively. We introduce a smart compression technique for feature selection and compare the performance from a subset of the AMI transcriptions with AMI-ASR output for the same subset.

Original language	Undefined
Title of host publication	First International IEEE Workshop on Spoken Language Technology SLT 2006
Editors	B. Raorke
Place of Publication	Palm Beach
Publisher	IEEE Computer Society
Pages	70-73
Number of pages	4
ISBN (Print)	1-4244-0873-3
DOIs	https://doi.org/10.1109/SLT.2006.326819
Publication status	Published - Dec 2006
Event	First International IEEE Workshop on Spoken Language Technology, SLT 2006 - Palm Beach, Aruba Duration: 10 Dec 2006 → 13 Dec 2006

Publication series

Name
Publisher	IEEE Computer Society
Number	10

Workshop

Workshop	First International IEEE Workshop on Spoken Language Technology, SLT 2006
Period	10/12/06 → 13/12/06
Other	10-13 December 2006

Keywords

EC Grant Agreement nr.: FP6/506811
EWI-9501
IR-67003
METIS-237662
HMI-CI: Computational Intelligence

Access to Document

10.1109/SLT.2006.326819

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Cite this

@inproceedings{c2a7d4f29d144c70b1b9c31b7b43b9b3,

title = "Dialogue-act tagging using smart feature selection: results on multiple corpora",

abstract = "This paper presents an overview of our on-going work on dialogueact classification. Results are presented on the ICSI, Switchboard, and on a selection of the AMI corpus, setting a baseline for forthcoming research. For these corpora the best accuracy scores obtained are 89.27%, 65.68% and 59.76%, respectively. We introduce a smart compression technique for feature selection and compare the performance from a subset of the AMI transcriptions with AMI-ASR output for the same subset.",

keywords = "EC Grant Agreement nr.: FP6/506811, EWI-9501, IR-67003, METIS-237662, HMI-CI: Computational Intelligence",

author = "Daan Verbree and R.J. Rienks and Heylen, {Dirk K.J.}",

year = "2006",

month = dec,

doi = "10.1109/SLT.2006.326819",

language = "Undefined",

isbn = "1-4244-0873-3",

publisher = "IEEE Computer Society",

number = "10",

pages = "70--73",

editor = "B. Raorke",

booktitle = "First International IEEE Workshop on Spoken Language Technology SLT 2006",

address = "United States",

note = "null ; Conference date: 10-12-2006 Through 13-12-2006",

}

Verbree, D, Rienks, RJ & Heylen, DKJ 2006, Dialogue-act tagging using smart feature selection: results on multiple corpora. in B Raorke (ed.), First International IEEE Workshop on Spoken Language Technology SLT 2006. IEEE Computer Society, Palm Beach, pp. 70-73, First International IEEE Workshop on Spoken Language Technology, SLT 2006, 10/12/06. https://doi.org/10.1109/SLT.2006.326819

Dialogue-act tagging using smart feature selection: results on multiple corpora. / Verbree, Daan; Rienks, R.J.; Heylen, Dirk K.J.

First International IEEE Workshop on Spoken Language Technology SLT 2006. ed. / B. Raorke. Palm Beach : IEEE Computer Society, 2006. p. 70-73.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - This paper presents an overview of our on-going work on dialogueact classification. Results are presented on the ICSI, Switchboard, and on a selection of the AMI corpus, setting a baseline for forthcoming research. For these corpora the best accuracy scores obtained are 89.27%, 65.68% and 59.76%, respectively. We introduce a smart compression technique for feature selection and compare the performance from a subset of the AMI transcriptions with AMI-ASR output for the same subset.

KW - EC Grant Agreement nr.: FP6/506811

KW - EWI-9501

KW - IR-67003

KW - METIS-237662

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