<?xml version="1.0" standalone="yes"?>
<Paper uid="W04-0109">
  <Title>Multilingual Noise-Robust Supervised Morphological Analysis using the WordFrame Model</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents the WordFrame model, a noise-robust supervised algorithm capable of inducing morphological analyses for languages which exhibit prefixation, suffixation, and internal vowel shifts. In combination with a n&amp;quot;aive approach to suffix-based morphology, this algorithm is shown to be remarkably effective across a broad range of languages, including those exhibiting infixation and partial reduplication. Results are presented for over 30 languages with a median accuracy of 97.5% on test sets including both regular and irregular verbal inflections. Because the proposed method trains extremely well under conditions of high noise, it is an ideal candidate for use in co-training with unsupervised algorithms.</Paragraph>
  </Section>
class="xml-element"></Paper>