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Publication Detail
Natural language text classification and filtering with trigrams and evolutionary nearest neighbour classifiers
  • Publication Type:
    Report
  • Authors:
    Langdon WB
  • publication date:
    07/2000
  • Place of publication:
    Kruislaan 413, NL-1098 SJ Amsterdam, The Netherlands
  • Report number:
    SEN-R0022
  • Print ISSN:
    1386-369X
  • Notes:
    email: W.Langdon@cs.ucl.ac.uk keywords: genetic algorithms, ngrams, trigrams, natural language processing, NLP notes: Also available as GECCO’2000 Late Breaking paper langdon:2000:ngramLB size: 10 pages
Abstract
Ngrams offer fast language independent multi-class text categorization. Text is reduced in a single pass to ngram vectors. These are assigned to one of several classes by a) nearest neighbour (KNN) and b) genetic algorithm operating on weights in a nearest neighbour classifier. 91 percent accuracy is found on binary classification on short multi-author technical English documents. This falls if more categories are used but 69 percent is obtained with 8 classes. Zipf law is found not to apply to trigrams.
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