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<Paper uid="N06-3005">
  <Title>Identifying Perspectives at the Document and Sentence Levels Using Statistical Models</Title>
  <Section position="8" start_page="229" end_page="229" type="concl">
    <SectionTitle>
6 Summary of Contributions
</SectionTitle>
    <Paragraph position="0"> In this paper we study the problem of learning to identify the perspective from which a text was written at the document and sentence levels. We show that perspectives are expressed in word usage, and statistical learning algorithms such as SVM and na&amp;quot;ive Bayes models can successfully uncover the word patterns chosen by authors from different perspectives. Furthermore, we develop a novel statistical model to infer how strongly a sentence convey perspective without any labels. By introducing latent variables, Latent Sentence Perspective Models are shown to capture well how perspectives are reflected at the document and sentence levels.</Paragraph>
    <Paragraph position="1"> The proposed statistical models can help analysts sift through a large collection of documents written from different perspectives. The unique sentence-level perspective modeling can automatically identify sentences that are strongly representative of the perspective of interest, and we plan to manually evaluate their quality in the future work.</Paragraph>
  </Section>
class="xml-element"></Paper>