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<Paper uid="W04-0109">
  <Title>Multilingual Noise-Robust Supervised Morphological Analysis using the WordFrame Model</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented the WordFrame model, a noise-robust supervised morphological analyzer which is highly successful across a broad range of languages.</Paragraph>
    <Paragraph position="1"> We have shown our model effective at learning morphologies which exhibit prefixation, suffixation, and stem-internal vowel changes. In addition, the WordFrame model was successful in handling the agglutination, infixation and partial reduplication found in languages such as Tagalog without explicitly modeling these phenomena. Most importantly, the WordFrame model is robust to large amounts of noise, making it an ideal candidate for use in co-training with lower-accuracy unsupervised algorithms. null</Paragraph>
  </Section>
class="xml-element"></Paper>