Browse by author
Lookup NU author(s): Dr Matias Garcia-Constantino
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
An investigation into the potential effectiveness of generating text classifiers from secondary data for the purpose of text summarisation is described. The application scenario assumes a questionnaire corpus where we wish to provide a summary regarding the nature of the free text element of such questionnaires, but no suitable training data is available. The advocated approach is to build the desired text summarisation classifiers using secondary data and then apply these classifiers, for the purpose of text summarisation, to the primary data. We refer to this approach using the acronym CGUSD (Classifier Generation Using Secondary Data). The approach is evaluated using real questionnaire data obtained as part of the SAVSNET (Small Animal Veterinary Surveillance Network) project.
Author(s): Garcia-Constantino MF, Coenen F, Noble PJ, Radford A, Setzkorn C, Tierney A
Editor(s): Petra Perner
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Machine Learning and Data Mining in Pattern Recognition: 7th International Conference (MLDM 2011)
Year of Conference: 2011
Publisher: Springer Berlin Heidelberg
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science