  The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and tested on a newswire text categorization task. This method, which
we call uncertainty sampling, reduced by as much as 500-fold the amount of
training data that would have to be manually classified to achieve a given
level of effectiveness.
