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<Paper uid="E06-3008">
  <Title>Towards Robust Animacy Classification Using Morphosyntactic Distributional Features</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents results from experiments in automatic classification of animacy for Norwegian nouns using decision-tree classifiers. The method makes use of relative frequency measures for linguistically motivated morphosyntactic features extracted from an automatically annotated corpus of Norwegian. The classifiers are evaluated using leave-one-out training and testing and the initial results are promising (approaching 90% accuracy)forhighfrequency nouns, however deteriorate gradually as lower frequency nouns are classified. Experiments attempting to empirically locate a frequency threshold for the classification method indicate that a subset of the chosen morphosyntactic features exhibit a notable resilience to data sparseness. Results will be presented which show that the classification accuracy obtained for high frequency nouns (with absolute frequencies &gt;1000) can be maintained for nouns with considerably lower frequencies ([?]50) by backing off to a smaller set of features at classification. null</Paragraph>
  </Section>
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