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We describe a method for classifying news stories using Memory Based Reasoning (MBR) a k-nearest neighbor method), that does not require manual topic definitions. Using an already coded training database of about 50,000 stories from the Dow Jones Press Release News Wire, and SEEKER Stanfill (a text retrieval system that supports relevance feedback) as the underlying match engine, codes are assigned to new, unseen stories with a recall of about 80% and precision of about 70%. There are about 350 different codes to be assigned. Using a massively parallel supercomputer, we leverage the information already contained in the thousands of coded stories and are able to code a story in about 2 seconds. Given SEEKER, the text retrieval system, we achieved these results in about two person-months. We believe this approach is effective in reducing the development time to implement classification systems involving large number of topics for the purpose of classification, message routing etc.
Masand et al. (Wed,) studied this question.
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