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Lookup NU author(s): Dr Matias Garcia-Constantino
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An investigation into the use of negation in Inductive Rule Learning (IRL) for text classification is described. The use of negated features in the IRL process has been shown to improve effectiveness of classification. However, although in the case of small datasets it is perfectly feasible to include the potential negation of all possible features as part of the feature space, this is not possible for datasets that include large numbers of features such as those used in text mining applications. Instead a process whereby features to be negated can be identified dynamically is required. Such a process is described in the paper and compared with established techniques (JRip, NaiveBayes, Sequential Minimal Optimization (SMO), OlexGreedy). The work is also directed at an approach to text classification based on a “bag of phrases” representation; the motivation here being that a phrase contains semantic information that is not present in single keyword. In addition, a given text corpus typically contains many more key-phrase features than keyword features, therefore, providing more potential features to be negated.
Author(s): Chua S, Coenen F, Malcom G, Garcia-Constantino MF
Editor(s): Max Bramer, Miltos Petridis and Lars Nolle
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Research and Development in Intelligent Systems XXVIII; Incorporating Applications and Innovations in Intelligent Systems XIX: Proceedings of AI-2011, the Thirty-first SGAI International Conference on Innovative Techniques and Applications of Artificial I
Year of Conference: 2011
Pages: 153-166
Publisher: Springer London
URL: http://dx.doi.org/10.1007/978-1-4471-2318-7_11
DOI: 10.1007/978-1-4471-2318-7_11
Library holdings: Search Newcastle University Library for this item
ISBN: 9781447123170
