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<Paper uid="P06-1064">
  <Title>Creating a CCGbank and a wide-coverage CCG lexicon for German</Title>
  <Section position="8" start_page="510" end_page="511" type="concl">
    <SectionTitle>
6 Conclusion and future work
</SectionTitle>
    <Paragraph position="0"> We have presented an algorithm which converts the syntax graphs in the German Tiger corpus (Brants et al., 2002) into Combinatory Categorial Grammar derivation trees. This algorithm is currently able to translate 92.4% of all graphs in Tiger, or 95.2% of all full sentences. Lexicons extracted from this corpus contain the correct entries for 86.7% of all and 94.2% of all seen tokens. Good lexical coverage is essential for the performance of statistical CCG parsers (Hockenmaier and Steedman, 2002a). Since the Tiger corpus contains complete morphological and lemma information for all words, future work will address the question of how to identify and apply a set of (non-recursive) lexical rules (Carpenter, 1992) to the extracted CCG lexicon to create a much larger lexicon. The number of lexical category types is almost twice as large as that of the English CCGbank. This is to be expected, since our grammar includes case features, and German verbs require different categories for main and subordinate clauses. We currently perform only the most essential preprocessing steps, although there are a number of constructions that might benefit from additional changes (e.g. comparatives, parentheticals, or fragments), both to increase coverage and accuracy of the extracted grammar.</Paragraph>
    <Paragraph position="1"> Since Tiger corpus is of comparable size to the Penn Treebank, we hope that the work presented here will stimulate research into statistical wide-coverage parsing of free word order languages such as German with deep grammars like CCG.</Paragraph>
  </Section>
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