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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.
@inproceedings{1009074, author = {Edel Greevy and Alan F. Smeaton}, title = {Classifying racist texts using a support vector machine}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {468--469}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009074}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
