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Segmenting broadcast news streams using lexical chains

Stokes, Nicola and Carthy, Joe and Smeaton, Alan F. (2002) Segmenting broadcast news streams using lexical chains. In: STAIRS 2002 - STarting Artificial Intelligence Researchers Symposium, 22-23 July 2002, Lyon, France.

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Abstract

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:IOS Press
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Enterprise Ireland
ID Code:324
Deposited On:13 Mar 2008 by DORAS Administrator. Last Modified 07 May 2010 12:05

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