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<Paper uid="P99-1014">
  <Title>Detlef Prescher</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> An important challenge in computational linguistics concerns the construction of large-scale computational lexicons for the numerous natural languages where very large samples of language use are now available. Resnik (1993) initiated research into the automatic acquisition of semantic selectional restrictions. Ribas (1994) presented an approach which takes into account the syntactic position of the elements whose semantic relation is to be acquired. However, those and most of the following approaches require as a prerequisite a fixed taxonomy of semantic relations. This is a problem because (i) entailment hierarchies are presently available for few languages, and (ii) we regard it as an open question whether and to what degree existing designs for lexical hierarchies are appropriate for representing lexical meaning. Both of these considerations suggest the relevance of inductive and experimental approaches to the construction of lexicons with semantic information.</Paragraph>
    <Paragraph position="1"> This paper presents a method for automatic induction of semantically annotated subcategorization frames from unannotated corpora. We use a statistical subcat-induction system which estimates probability distributions and corpus frequencies for pairs of a head and a subcat frame (Carroll and Rooth, 1998). The statistical parser can also collect frequencies for the nominal fillers of slots in a subcat frame. The induction of labels for slots in a frame is based upon estimation of a probability distribution over tuples consisting of a class label, a selecting head, a grammatical relation, and a filler head. The class label is treated as hidden data in the EMframework for statistical estimation.</Paragraph>
  </Section>
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