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<Paper uid="W02-1410">
  <Title>Question Terminology and Representation for Question Type Classication</Title>
  <Section position="5" start_page="5" end_page="5" type="relat">
    <SectionTitle>
4 Related Work
</SectionTitle>
    <Paragraph position="0">  Recently,withaneedtoincorporateuserpreferences in information retrieval, several work has been done which classies documents by genre.</Paragraph>
    <Paragraph position="1"> For instance, (Finn et al., 2002) used machine learningtechniquesto identifysubjective (opinion)documentsfromnewspaperarticles. Todetermine what feature adapts well to unseen domains, they compared three kinds of features: words, part-of-speech statistics and manually selected meta-linguistic features. They concluded that the part-of-speech performed the best with regard to domain transfer. However, not onlywere their feature sets pre-determined, their features were distinct from words in the documents (or features were the entire words themselves), thus no feature subset selection was performed.</Paragraph>
    <Paragraph position="2"> (Wiebe, 2000) also used machine learning techniques to identify subjectivesentences. She focused on adjectives as an indicator of subjectivity, and used corpus statistics and lexical semantic information to derive adjectives that yielded high precision.</Paragraph>
  </Section>
class="xml-element"></Paper>