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<Paper uid="W06-2911">
  <Title>Applying Alternating Structure Optimization to Word Sense Disambiguation</Title>
  <Section position="4" start_page="77" end_page="77" type="intro">
    <SectionTitle>
2 Alternating structure optimization
</SectionTitle>
    <Paragraph position="0"> This section gives a brief summary of ASO. We first introduce a standard linear prediction model for a single task and then extend it to a joint linear model used by ASO.</Paragraph>
    <Section position="1" start_page="77" end_page="77" type="sub_section">
      <SectionTitle>
2.1 Standard linear prediction models
</SectionTitle>
      <Paragraph position="0"> In the standard formulation of supervised learning, we seek a predictor that maps an input vector (or feature vector) DC BE CG to the corresponding output DD BE CH. For NLP tasks, binary features are often used - for example, if the word to the left is &amp;quot;money&amp;quot;, set the corresponding entry of DC to 1; otherwise, set it to 0. A CZ-way classification problem can be cast as CZ binary classification problems, regarding output DD BP B7BD and DD BP A0BD as &amp;quot;in-class&amp;quot; and &amp;quot;out-of-class&amp;quot;, respectively.</Paragraph>
      <Paragraph position="1"> Predictors based on linear prediction models take the form: CUB4DCB5 BP DBCCDC, where DB is called a weight vector. A common method to obtain a predictor</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>