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Proceedings chapter
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Word Distributions for Thematic Segmentation in a Support Vector Machine Approach

Contributors

Georgescul, Maria; Clark, Alexander; Armstrong, Susan

Presented at

New York (USA), 8-9 June

Published in

Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), p. 101-108

Publication date

2006

Abstract

We investigate the appropriateness of using a technique based on support vector machines for identifying thematic structure of text streams. The thematic segmentation task is modeled as a binary classification problem, where the different classes correspond to the presence or the absence of a thematic boundary. Experiments are conducted with this approach by using features based on word distributions through text. We provide empirical evidence that our approach is robust, by showing good performance on three different data sets. In particular, substantial improvement is obtained over previously published results of word distribution based systems when evaluation is done on a corpus of recorded and transcribed multi-party dialogs.

UNIGE affiliation entities

Citation (ISO format)

GEORGESCUL, Maria, CLARK, Alexander, ARMSTRONG, Susan. Word Distributions for Thematic Segmentation in a Support Vector Machine Approach. In: Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X). New York (USA). [s.l.] : [s.n.], 2006. p. 101–108.

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Proceedings chapter

Identifiers

  • PID : unige:3490
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Technical informations

Creation

02/10/2009 09:31:28

First validation

02/10/2009 09:31:28

Update

14/03/2023 15:14:59

Status update

14/03/2023 15:14:59

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