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<Paper uid="W05-0804">
  <Title>Bilingual Word Spectral Clustering for Statistical Machine Translation</Title>
  <Section position="6" start_page="31" end_page="31" type="concl">
    <SectionTitle>
5 Discussions and Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, a new approach for bilingual word clustering using eigenstructure in bilingual feature space is proposed. Eigenvectors from this feature space are considered as bilingual concepts. Bilingual clusters from the subspaces expanded by these concepts are inferred with high semantic correlations within each cluster, and high translation qualities across clusters from the two languages.</Paragraph>
    <Paragraph position="1"> Our empirical study also showed effectiveness of using bilingual word clusters in extended HMMs for statistical machine translation. The K-means based clustering algorithm can be easily extended to do hierarchical clustering. However, extensions of translation models are needed to leverage the hierarchical clusters appropriately.</Paragraph>
  </Section>
class="xml-element"></Paper>