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<Paper uid="W04-2008">
  <Title>An Algorithm for Open Text Semantic Parsing</Title>
  <Section position="8" start_page="7" end_page="7" type="relat">
    <SectionTitle>
7 Related Work
</SectionTitle>
    <Paragraph position="0"> There are several statistical approaches for automatic semantic role labeling based on PropBank and FrameNet. (Gildea and Jurafsky, 2000) proposed a statistical approach based on FrameNet I data for annotation of semantic roles. Fleischman (Fleischman et al., 2003) used FrameNet annotations in a maximum entropy framework. A more flexible generative model is proposed in (Thompson et al., 2003), where null-instantiated roles can be also identified, and frames are not assumed to be known a-priori. These approaches exclusively focus on semantic roles labeling based on statistical methods, rather than analysis of the full structure of sentence semantics. However, a rule-based approach is closer to the way humans interpret the semantic structure of a sentence. Moreover, as mentioned earlier, the FrameNet data is not meant to be &amp;quot;statistically representative&amp;quot; (Johnson et al., 2002), but rather illustrative for various language constructs, and therefore a rule-based approach is more suitable for this lexical resource.</Paragraph>
  </Section>
class="xml-element"></Paper>