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<Paper uid="P02-1045">
  <Title>Applying Co-Training to Reference Resolution</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> A major obstacle for natural language processing systems which analyze natural language texts or utterances is the need to identify the entities referred to by means of referring expressions. Among referring expressions, pronouns and definite noun phrases (NPs) are the most prominent.</Paragraph>
    <Paragraph position="1"> Supervised machine learning algorithms were used for pronoun resolution with good results (Ge et al., 1998), and for definite NPs with fairly good results (Aone and Bennett, 1995; McCarthy and Lehnert, 1995; Soon et al., 2001). However, the deficiency of supervised machine learning approaches is the need for an unknown amount of annotated training data for optimal performance.</Paragraph>
    <Paragraph position="2"> So, researchers in NLP began to experiment with weakly supervised machine learning algorithms such as Co-Training (Blum and Mitchell, 1998).</Paragraph>
    <Paragraph position="3"> Among others Co-Training was applied to document classification (Blum and Mitchell, 1998), named-entity recognition (Collins and Singer, 1999), noun phrase bracketing (Pierce and Cardie, 2001), and statistical parsing (Sarkar, 2001). In this paper we apply Co-Training to the problem of reference resolution in German texts from the tourism domain in order to provide answers to the following questions: a0 Does Co-Training work at all for this task (when compared to conventional C4.5 decision tree learning)? a0 How much labeled training data is required for achieving a reasonable performance? First, we discuss features that have been found to be relevant for the task of reference resolution, and describe the feature set that we are using (Section 2). Then we briefly introduce the Co-Training paradigm (Section 3), which is followed by a description of the corpus we use, the corpus annotation, and the way we prepared the data for using a binary classifier in the Co-Training algorithm (Section 4). In Section 5 we specify the experimental setup and report on the results.</Paragraph>
  </Section>
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