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<Paper uid="W04-2704">
  <Title>Proposition Bank II: Delving Deeper</Title>
  <Section position="3" start_page="1" end_page="1" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> An important question is the degree to which current statistical NLP systems can be made more domain-independent without prohibitive costs, either in terms of engineering or annotation. The Proposition Bank is designed as a broad-coverage resource to facilitate the development of more general systems. It focuses on the argument structure of verbs, and provides a complete corpus annotated with semantic roles, including participants traditionally viewed as arguments and adjuncts. Correctly identifying the semantic roles of the sentence constituents is a crucial part of interpreting text, and in addition to forming a component of the information extraction problem, can serve as an intermediate step in machine translation or automatic summarization.</Paragraph>
    <Paragraph position="1"> The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic roles, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent co-reference, quantification, and many other higher-order phenomena, but also broad, in that it covers every verb in the corpus and allows representative statistics to be calculated. The semantic annotation provided by PropBank is only a first approximation at capturing the full richness of semantic representation.</Paragraph>
    <Paragraph position="2"> Additional annotation of nominalizations and other noun predicates has already begun at NYU. This paper presents an overview of the second phase of PropBank Annotation, PropBank II, which is being applied to English and Chinese and includes (Neodavidsonian) eventuality variables, nominal references, sense tagging, and discourse connectives.</Paragraph>
  </Section>
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