Dialog Act Classification Using N-Gram Algorithms

Max Louwerse; Scott Crossley

Dialog Act Classification Using N-Gram Algorithms

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

Abstract

Speech act classification remains one of the challenges in natural language processing. This paper evaluates a classification system that assigns one of twelve dialog acts to an utterance from the Map Task Corpus. The dialog act classification system chooses a dialog act based on n-grams form a training set. The system's performance is comparable to other classification systems, like those using support vector machines. Performance is high given the fact that the system only considers an utterance out of context and from written input only. Moreover, the system's performance is on par with human performance.

Original language	English
Title of host publication	Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference
Editors	Geoff Sutcliffe, Randy Goebel
Place of Publication	Menlo Park
Publisher	AAAI Press
Pages	758-763
Publication status	Published - 2006
Externally published	Yes
Event	19th International Florida Artificial Intelligence Research Society Conference - Melbourne Beach, United States Duration: 11 May 2006 → 13 May 2006

Conference

Conference	19th International Florida Artificial Intelligence Research Society Conference
Country/Territory	United States
City	Melbourne Beach
Period	11/05/06 → 13/05/06

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Cite this

@inproceedings{c1c2d1b5c36b4b25a2b318f03eb13df9,

title = "Dialog Act Classification Using N-Gram Algorithms",

abstract = "Speech act classification remains one of the challenges in natural language processing. This paper evaluates a classification system that assigns one of twelve dialog acts to an utterance from the Map Task Corpus. The dialog act classification system chooses a dialog act based on n-grams form a training set. The system's performance is comparable to other classification systems, like those using support vector machines. Performance is high given the fact that the system only considers an utterance out of context and from written input only. Moreover, the system's performance is on par with human performance.",

author = "Max Louwerse and Scott Crossley",

year = "2006",

language = "English",

pages = "758--763",

editor = "Geoff Sutcliffe and Randy Goebel",

booktitle = "Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference",

publisher = "AAAI Press",

note = "19th International Florida Artificial Intelligence Research Society Conference ; Conference date: 11-05-2006 Through 13-05-2006",

}

Louwerse, M & Crossley, S 2006, Dialog Act Classification Using N-Gram Algorithms. in G Sutcliffe & R Goebel (eds), Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference. AAAI Press, Menlo Park, pp. 758-763, 19th International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, Florida, United States, 11/05/06. <https://pdfs.semanticscholar.org/0878/2e1bf9cac6c255a17f766809b135c910e018.pdf>

Dialog Act Classification Using N-Gram Algorithms. / Louwerse, Max; Crossley, Scott.
Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference. ed. / Geoff Sutcliffe; Randy Goebel. Menlo Park: AAAI Press, 2006. p. 758-763.

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

TY - GEN

T1 - Dialog Act Classification Using N-Gram Algorithms

AU - Louwerse, Max

AU - Crossley, Scott

PY - 2006

Y1 - 2006

N2 - Speech act classification remains one of the challenges in natural language processing. This paper evaluates a classification system that assigns one of twelve dialog acts to an utterance from the Map Task Corpus. The dialog act classification system chooses a dialog act based on n-grams form a training set. The system's performance is comparable to other classification systems, like those using support vector machines. Performance is high given the fact that the system only considers an utterance out of context and from written input only. Moreover, the system's performance is on par with human performance.

AB - Speech act classification remains one of the challenges in natural language processing. This paper evaluates a classification system that assigns one of twelve dialog acts to an utterance from the Map Task Corpus. The dialog act classification system chooses a dialog act based on n-grams form a training set. The system's performance is comparable to other classification systems, like those using support vector machines. Performance is high given the fact that the system only considers an utterance out of context and from written input only. Moreover, the system's performance is on par with human performance.

M3 - Conference contribution

SP - 758

EP - 763

BT - Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference

A2 - Sutcliffe, Geoff

A2 - Goebel, Randy

PB - AAAI Press

CY - Menlo Park

T2 - 19th International Florida Artificial Intelligence Research Society Conference

Y2 - 11 May 2006 through 13 May 2006

ER -

Dialog Act Classification Using N-Gram Algorithms

Abstract

Conference

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