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<Paper uid="C04-1179">
  <Title>FrameNet-based Semantic Parsing using Maximum Entropy Models</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> To produce a semantic analysis has long been a goal of Computational Linguistics. To do so, however, requires a representation of the semantics of each predicate. Since each predicate may have a particular collection of semantic roles (agent, theme, etc.) the first priority is to build a collection of predicate senses with their associated role frames. This task is being performed in the FrameNet project based on frame semantics (Fillmore, 1976).</Paragraph>
    <Paragraph position="1"> Each frame contains a principal lexical item as the target predicate and associated frame-specific roles, such as offender and buyer, called frame elements. FrameNet I contains 1,462 distinct predicates (927 verbs, 339 nouns, 175 adjectives) in 49,000 annotated sentences with 99,000 annotated frame elements. Given these, it would be interesting to attempt an automatic sentence interpretation.</Paragraph>
    <Paragraph position="2"> We build semantic parsing based on FrameNet, treating it as a classification problem. We split the problem into three parts: sentence segmentation, frame element identification for each segment, and semantic role tagging for each frame element. In this paper, we provide a pipeline framework of these three phases, followed by a step of re-ranking from n-best lists of every phase for the final output. All classification and re-ranking are performed by Maximum Entropy.</Paragraph>
    <Paragraph position="3"> The top-five final outputs provide an F-score of 76.2% for the correct frame element identification and semantic role tagging. The performance of the single best output is 61.5% F-score.</Paragraph>
    <Paragraph position="4"> The rest of the paper is organized as follows: we review related work in Section 2, explain Maximum Entropy in Section 3, describe the detailed method in Section 4, show the re-ranking process in Section 5, and conclude in Section 6.</Paragraph>
  </Section>
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