<?xml version="1.0" standalone="yes"?>
<Paper uid="W98-1125">
  <Title>Discourse Parsing: A Decision Tree Approach</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The paper presents a new statistical method, for parsing discourse. A parse of discourse is defined as a set of semantic dependencies among sentences that make up the discourse. A collection of news articles from a Japanese economics daily are manually marked for dependency and used as a training/testing corpus. We use a C4.5 decision tree method to develop a model of sentential dependencies. However, rather than to use class decisions made by C4.5, we exploit information on class distributions to rank possible dependencies among sentences according to their probabilistic strength and take a parse to be a set of highest ranking dependencies. We also study effects of features such as clue words, distance and similarity on the performance of the discourse parser. Experiments have found that the method performs reasonably well on diverse text types, scoring an accuracy rate of over 60%.</Paragraph>
  </Section>
class="xml-element"></Paper>