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<Paper uid="P06-2117">
  <Title>Boosting Statistical Word Alignment Using Labeled and Unlabeled Data</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semi-supervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based on this semi-supervised boosting algorithm, we investigate two boosting methods for word alignment. In addition, we improve the word alignment results by combining the results of the two semi-supervised boosting methods. Experimental results on word alignment indicate that semi-supervised boosting achieves relative error reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively.</Paragraph>
  </Section>
class="xml-element"></Paper>