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<Paper uid="N06-1045">
  <Title>A Better -BestList: Practical Determinization ofWeighted Finite Tree Automata</Title>
  <Section position="7" start_page="356" end_page="357" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have shown that weighted determinization is useful for recovering -best unique trees from a weighted forest. As summarized in Figure 9, the  This parser acquires a small subset of subtrees, in contrast with DOP, and the beam search for this problem has not been optimized.</Paragraph>
    <Paragraph position="1">  after determinization is applied to the forests, removing duplicate trees.</Paragraph>
    <Paragraph position="2"> number of repeated trees prior to determinization wastypically verylarge, andthus determinization is critical to recovering true tree weight. We have improved evaluation scores by incorporating the presented algorithm into our MT work and we believe that other NLP researchers working with trees can similarly benefit from this algorithm.</Paragraph>
    <Paragraph position="3"> Further advances in determinization will provide additional benefit to the community. The translation system detailed here is a string-to-tree system, andthedeterminization algorithm returns the -best unique trees fromapacked forest. UsersofMTsystems are generally interested in the string yield of those trees, and not the trees per se. Thus, an algorithm that can return the -best unique strings from apacked forest would be auseful extension.</Paragraph>
    <Paragraph position="4"> We plan for our weighted determinization algorithm to be one component in a generally available treeautomatapackageforintersection, composition, training, recognition, and generation of weighted and unweighted tree automata for research tasks such as the ones described above.</Paragraph>
  </Section>
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