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<Paper uid="C02-1054">
  <Title>Efficient Support Vector Classifiers for Named Entity Recognition</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories such as person, organization, and date. It is a key technology of Information Extraction and Open-Domain Question Answering. First, we show that an NE recognizer based on Support Vector Machines (SVMs) gives better scores than conventional systems. However, off-the-shelf SVM classifiers are too inefficient for this task. Therefore, we present a method that makes the system substantially faster.</Paragraph>
    <Paragraph position="1"> This approach can also be applied to other similar tasks such as chunking and part-of-speech tagging. We also present an SVM-based feature selection method and an efficient training method.</Paragraph>
  </Section>
class="xml-element"></Paper>