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Hierarchical rule generalisation for speaker identification in fiction books

Glass, Kevin and Bangay, Shaun 2006, Hierarchical rule generalisation for speaker identification in fiction books, in SAICSIT '06 : Research for a changing world : Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries, South African Institute for Computer Scientists and Information Technologists, Pretoria, South Africa, pp. 31-40.

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Title Hierarchical rule generalisation for speaker identification in fiction books
Author(s) Glass, Kevin
Bangay, Shaun
Conference name South African institute of computer scientists and information technologists (2006 : Somerset West, South Africa)
Conference location Somerset West, South Africa
Conference dates 9-11 Oct. 2006
Title of proceedings SAICSIT '06 : Research for a changing world : Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Editor(s) Bishop, Judith
Kourie, Derrick
Publication date 2006
Series ACM conference proceedings series
Conference series South African Institute for Computer Scientists and Information Technologists Conference
Start page 31
End page 40
Publisher South African Institute for Computer Scientists and Information Technologists
Place of publication Pretoria, South Africa
Keyword(s) pattern matching
machine Learning
generalisation
Summary This paper presents a hierarchical pattern matching and generalisation technique which is applied to the problem of locating the correct speaker of quoted speech found in fiction books. Patterns from a training set are generalised to create a small number of rules, which can be used to identify items of interest within the text. The pattern matching technique is applied to finding the Speech-Verb, Actor and Speaker of quotes found in ction books. The technique performs well over the training data, resulting in rule-sets many times smaller than the training set, but providing very high accuracy. While the rule-set generalised from one book is less effective when applied to different books than an approach based on hand coded heuristics, performance is comparable when testing on data closely related to the training set.
ISBN 9781595935670
1595935673
Language eng
Field of Research 080107 Natural Language Processing
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, SAICSIT
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039201

Document type: Conference Paper
Collection: School of Information Technology
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