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<Paper uid="W05-0626">
  <Title>Semantic Role Labeling via Consensus in Pattern-Matching</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Semantic role labeling is to find all arguments for all predicates in a sentence, and classify them by semantic roles such as A0, A1, AM-TMP and so on. The performance of semantic role labeling can play a key role in Natural Language Processing applications, such as Information Extraction, Question Answering, and Summarization (Pradhan et al., 2004).</Paragraph>
    <Paragraph position="1"> Most existing systems separate semantic role labeling into two sub-problems, boundary recognition and role classification, and use feature-based models to address both (Carreras et al., 2004). Our strategy is to develop a boundary analyzer by a general tree-based predicate-argument recognition algorithm (GT-PARA) for boundary recognition, and a pattern-matching model for role classification. The only information used in our system is Charniak's annotation with words, which contains all useful syntactic annotations. Five features, which are Headword, Phrase type, Voice, Target verb, and Preposition (of the first word), and a Pattern set, which includes numbers and types of roles in a pattern, are used for the pattern-matching approach. We develop a Pattern Database, trained by Wall Street Journal section 02 to 21, as our knowledge/Data base. The system outline is described in the following section.</Paragraph>
  </Section>
class="xml-element"></Paper>