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<Paper uid="N03-1025">
  <Title>Language and Task Independent Text Categorization with Simple Language Models</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic principles and achieves effective performance across a variety of languages and tasks without requiring feature selection or extensive pre-processing. To demonstrate the language and task independence of the proposed technique, we present experimental results on several languages--Greek, English, Chinese and Japanese--in several text categorization problems--language identification, authorship attribution, text genre classification, and topic detection. Our experimental results show that the simple approach achieves state of the art performance in each case.</Paragraph>
  </Section>
class="xml-element"></Paper>