Combining multiple knowledge sources for discourse segmentation

Diane J. Litman; Rebecca J. Passonneau

Combining multiple knowledge sources for discourse segmentation

Diane J. Litman, Rebecca J. Passonneau

Computer Science and Engineering

Research output: Contribution to journal › Conference article › peer-review

65 Scopus citations

Abstract

We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).

Original language	English (US)
Pages (from-to)	108-115
Number of pages	8
Journal	Proceedings of the Annual Meeting of the Association for Computational Linguistics
Volume	1995-June
State	Published - 1995
Event	33rd Annual Meeting of the Association for Computational Linguistics, ACL 1995 - Cambridge, United States Duration: Jun 26 1995 → Jun 30 1995

All Science Journal Classification (ASJC) codes

Computer Science Applications
Linguistics and Language
Language and Linguistics

Cite this

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title = "Combining multiple knowledge sources for discourse segmentation",

abstract = "We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).",

author = "Litman, {Diane J.} and Passonneau, {Rebecca J.}",

note = "Publisher Copyright: {\textcopyright} ACL 1995.All right reserved.; 33rd Annual Meeting of the Association for Computational Linguistics, ACL 1995 ; Conference date: 26-06-1995 Through 30-06-1995",

year = "1995",

language = "English (US)",

volume = "1995-June",

pages = "108--115",

journal = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

issn = "0736-587X",

publisher = "Association for Computational Linguistics (ACL)",

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TY - JOUR

T1 - Combining multiple knowledge sources for discourse segmentation

AU - Litman, Diane J.

AU - Passonneau, Rebecca J.

PY - 1995

Y1 - 1995

N2 - We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).

AB - We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).

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UR - http://www.scopus.com/inward/citedby.url?scp=85149105072&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85149105072

SN - 0736-587X

VL - 1995-June

SP - 108

EP - 115

JO - Proceedings of the Annual Meeting of the Association for Computational Linguistics

JF - Proceedings of the Annual Meeting of the Association for Computational Linguistics

T2 - 33rd Annual Meeting of the Association for Computational Linguistics, ACL 1995

Y2 - 26 June 1995 through 30 June 1995

ER -

Combining multiple knowledge sources for discourse segmentation

Abstract

All Science Journal Classification (ASJC) codes

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