Word Distributions for Thematic Segmentation in a Support Vector Machine Approach

Maria Georgescul; Alexander Clark; Susan Armstrong

Word Distributions for Thematic Segmentation in a Support Vector Machine Approach

Maria Georgescul, Alexander Clark, Susan Armstrong

Philosophy

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

11 Citations (Scopus)

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.

Original language	Undefined/Unknown
Title of host publication	Proceedings of CoNLL
Pages	101-108
Number of pages	8
Publication status	Published - 2006

Cite this

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title = "Word Distributions for Thematic Segmentation in a Support Vector Machine Approach",

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.",

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T1 - Word Distributions for Thematic Segmentation in a Support Vector Machine Approach

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AU - Clark, Alexander

AU - Armstrong, Susan

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N2 - 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.

AB - 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.

M3 - Conference paper

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EP - 108

BT - Proceedings of CoNLL

ER -