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<Paper uid="W97-0307">
  <Title>Tagging Grammatical Functions</Title>
  <Section position="11" start_page="511" end_page="511" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> A German newspaper corpus is currently being annotated with a new annotation scheme especially designed for free word order languages.</Paragraph>
    <Paragraph position="1"> Two levels of automatic annotation (level 1: assigning grammatical functions and level 2: assigning phrase categories) have been presented and evaluated in this paper.</Paragraph>
    <Paragraph position="2"> The overall accuracy for assigning grammatical functions is 94.2%, ranging from 89% to 98%, depending on the type of phrase. The least accuracy is achieved for sentences, the best for prepositional phrases. By suppressing unreliable decisions, precision can be increased to range from 92% to 99%.</Paragraph>
    <Paragraph position="3"> The overall accuracy for assigning phrase categories is 95.4%, ranging from 89% to 99%, depending the category. By suppressing unreliable decisions, precision can also be increased to range from 92% to over 99%.</Paragraph>
    <Paragraph position="4"> In the error analysis, the following sources of misinterpretation could be identified: insufficient linguistic information in the nodes (e.g., missing case information), and insufficient information about the global structure of phrases (e.g., missing valency information). Morphological information in the tagset, for example, helps to identify the objects and the subject of a sentence. Using a more fine-grained tagset, however, requires methods for adjusting the granularity of the tagset to the size (and coverage) of the corpus, in order to cope with the sparse data problem.</Paragraph>
  </Section>
class="xml-element"></Paper>