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<Paper uid="W98-1105">
  <Title>Semantic Tagging using a Probabilistic Context Free Grammar *</Title>
  <Section position="14" start_page="47" end_page="48" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have shown that a simple statistical model can identify semantic slot-fillers in a management succession task with 83% accuracy (F-measure with a score of 0.5 for partial matches). The system was trained on only 560 sentences, with the additional requirements of only a part-of-speech tagger and a morphological analyser. We initially considered a finite-state approach similar to that used for POS tagging (Church 88), or named-entity identification (Bikel et al. 97), but argued that the Markov approximation gives a poor model for this task. The alternative, which has a PCFG component to define the probability of the underlying sequence of labels, allows a good parameterization of the problem, and can be decoded efficiently using the CKY algorithm. Finally, we believe that the framework presented in this paper can be extended to model the complete information extraction process.</Paragraph>
  </Section>
class="xml-element"></Paper>