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<Paper uid="W06-1620">
  <Title>Multilingual Deep Lexical Acquisition for HPSGs via Supertagging</Title>
  <Section position="10" start_page="169" end_page="169" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we have explored a method for learning new lexical items for HPSG-based precision grammars through supertagging. Our pseudo-likelihood conditional random field-based approach provides a principled way of learning a supertagger from tens-of-thousands of training sentences and with hundreds of possible tags.</Paragraph>
    <Paragraph position="1"> We achieve start-of-the-art results for both English and Japanese data sets with a largely language-independent feature set. Our model also achieves performance at the type- and token-level, over different word classes and at multiword expression identification, superior to a probabilistic baseline and a transformation based learning approach. null</Paragraph>
  </Section>
class="xml-element"></Paper>