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<Paper uid="W06-0203">
  <Title>Parser Errors</Title>
  <Section position="9" start_page="31" end_page="31" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we argued that definitions are an important element of legal texts and in particular of court decisions. We provided a structural segmentation scheme for definitions and discussed a method of applying computational linguistic analysis techniques for their text-based extraction and automatic segmentation. We showed that a large number of definitions can in fact be extracted at high precision using this method, but we also pointed out that there is still much room for improvement in terms of recall, e.g. through the inclusion of further definition patterns.</Paragraph>
    <Paragraph position="1"> Our future work in this area will focus on the integration of extraction results across documents (e.g. recognizing and collecting complementary definitions for the same concept) and on a user interface for structured access to this data. For this work we have access to a corpus of several million verdicts provided to us by the company juris GmbH, Saarbrucken. We also demonstrated how the identification of definitions can improve the results of text-driven ontology learning in the legal domain. When looking for noun-adjective bigrams encoding relevant concepts, it leads to a considerable increase in precision to restrict the search to definienda only. This method is more precise than selecting the top ranks of a log-likelihood ranking. Its great disadvantage is the very low total number of results, leading to poor recall. However by combining a log-likelihood ranking with definition-based concept extraction, recall can be improved while still achieving better precision than with a log-likelihood ranking alone. Moreover this combined method also retrieves concepts that are too infrequent to be included at all in a log-likelihood ranking.</Paragraph>
    <Paragraph position="2"> There is however another, maybe even more relevant reason to look for definitions in ontology learning. Definitions in legal text often very explicitly and precisely determine all kinds of relational knowledge about the defined concept.</Paragraph>
    <Paragraph position="3"> For instance they specify explicit subordinations (as in the classical definitio per genus et differen116 140 181 41  tiam), introduce restrictions on roles inherited from a superconcept, determine the constitutive parts of the definiendum, or contain information about its causal relations to other concepts. As one focus of our future work we plan to investigate how such rich ontological knowledge can be extracted automatically.</Paragraph>
  </Section>
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