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<Paper uid="E06-1014">
  <Title>Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable.</Paragraph>
    <Paragraph position="1"> Inthispaper wepresent amethodforusing LSA analysis to initialize a PLSA model.</Paragraph>
    <Paragraph position="2"> We also investigated the performance of our method for the tasks of text segmentation and retrieval onpersonal-size corpora, and present results demonstrating the efficacy of our proposed approach.</Paragraph>
  </Section>
class="xml-element"></Paper>