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Classifying racist texts using a support vector machine

Greevy, Edel and Smeaton, Alan F. (2004) Classifying racist texts using a support vector machine. In: SIGIR 2004 - the 27th Annual International ACM SIGIR Conference, 25-29 July 2004, Sheffield, UK.

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Abstract

In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, and in this poster we look at three representations of a web page within an SVM -- bag-of-words, bigrams and part-of-speech tags.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Text Categorisation/Classification; Machine Learning; Support Vector Machines;
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Association for Computing Machinery
Official URL:http://dx.doi.org/10.1145/1008992.1009074
Copyright Information:© ACM, 2004. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution
ID Code:368
Deposited On:28 Mar 2008 by DORAS Administrator. Last Modified 04 Feb 2009 11:32

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