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<Paper uid="W04-1116">
  <Title>Automatic Semantic Role Assignment for a Tree Structure</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present an automatic semantic roles labeling system for structured trees of Chinese sentences. It adopts dependency decision making and example-based approaches. The training data and extracted examples are from the Sinica Treebank, which is a Chinese Treebank with semantic role assigned for each constituent. It used 74 abstract semantic roles including thematic roles, such as 'agent'; 'theme', 'instrument', and secondary roles of 'location', 'time', 'manner' and roles for nominal modifiers. The design of role assignment algorithm is based on the different decision features, such as head-argument/modifier, case makers, sentence structures etc. It labels semantic roles of parsed sentences. Therefore the practical performance of the system depends on a good parser which labels the right structures of sentences. The system achieves 92.71% accuracy in labeling the semantic roles for pre-structure- bracketed texts which is considerably higher than the simple method using probabilistic model of head-modifier relations.</Paragraph>
  </Section>
class="xml-element"></Paper>