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<Paper uid="W97-1005">
  <Title>A Statistical Decision Making Method: A Case Study on Prepositional Phrase Attachment*</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Decision problems are classically defined as problems whose answers fall in either of two classes: Yes and No (Garey and Johnson, 1979). Optimization problems are another class of problems that maximize or minimize some value; however, they can be cast as decision problems as well (Cormen et al., 1990).</Paragraph>
    <Paragraph position="1"> Classification problems incorporate the characteristics of both: A classification problem is a decision *This research was supported in part by the Office of Naval Research under grant number N00014-95-1-0776 problem, in which a decision is made (a class is selected) that maximizes a utility function (yon Neumann and Morgenstern, 1953). The Model Switching method as proposed in this paper can be used with any utility function (decision criterion) for any decision problem with categorical data that can be represented as a tuple (C, F1, F2, ..., Fn) of a class variable C and some feature variables F{1 .... }.</Paragraph>
    <Paragraph position="2"> In the following sections, we will describe the Prepositional Phrase Attachment (PPA) problem and various approaches to solving it. After discussing the statistical concepts used in this work, we will introduce the concept of Model Switching, why it is needed, how it works, and our experience on the PPA problem with Model Switching. Comparisons with earlier works on corpus-based PPA prediction and conclusions will follow.</Paragraph>
  </Section>
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