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<Paper uid="W03-1015">
  <Title>Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms</Title>
  <Section position="3" start_page="1" end_page="2" type="intro">
    <SectionTitle>
2 Noun Phrase Coreference Resolution
</SectionTitle>
    <Paragraph position="0"> Noun phrase coreference resolution refers to the problem of determining which noun phrases (NPs) refer to each real-world entity mentioned in a document. null  In this section, we give an overview of the coreference resolution system to which the boot- null Concrete examples of the coreference task can be found in MUC-6 (1995) and MUC-7 (1998).</Paragraph>
    <Paragraph position="1"> strapping algorithms will be applied.</Paragraph>
    <Paragraph position="2"> The framework underlying the coreference system is a standard combination of classification and clustering (see Ng and Cardie (2002) for details). Coreference resolution is first recast as a classification task, in which a pair of NPs is classified as co-referring or not based on constraints that are learned from an annotated corpus. A separate clustering mechanism then coordinates the possibly contradictory pairwise classifications and constructs a partition on the set of NPs. When the system operates within the weakly supervised setting, a weakly supervised algorithm bootstraps the coreference classifier from the given labeled and unlabeled data rather than from a much larger set of labeled instances. The clustering algorithm, however, is not manipulated by the bootstrapping procedure.</Paragraph>
  </Section>
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