<?xml version="1.0" standalone="yes"?>
<Paper uid="W00-1305">
  <Title>Topic Analysis Using a Finite Mixture Model</Title>
  <Section position="11" start_page="42" end_page="43" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have proposed a new method of topic analysis that employs a finite mixture model, referred to here as a stochastic topic model (STM).</Paragraph>
    <Paragraph position="1"> Topic analysis consists of two main tasks: text segmentation and topic identification. With topic analysis, one can obtain a topic structure for a text.</Paragraph>
    <Paragraph position="2"> Our method addresses topic analysis within a single framework. It has the following novel features: 1) it represents topics by means of word dusters and employs a finite mixture model (STM) to represent a word distribution within a text; 2) it constructs topics on the basis of corpus data before conducting topic analysis; 3) it segments a text by detecting significant differences between STMs; and 4) it identifies topics by estimating parameters 1degHere, k was set to 5 because the average length of a text was about 10 sentences. ....</Paragraph>
    <Paragraph position="3"> llWe will discuss the results in the full version of the paper.</Paragraph>
    <Paragraph position="4">  rec. pre. err. rec. pre. err.</Paragraph>
    <Paragraph position="5">  of STMs.</Paragraph>
    <Paragraph position="6"> Experimental results indicate that our method outperforms a method that combines existing techniques. More specifically, it significantly outperforms the combined method in topic identification.</Paragraph>
  </Section>
class="xml-element"></Paper>