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<Paper uid="W06-0802">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Hybrid Systems for Information Extraction and Question Answering</Title>
  <Section position="6" start_page="15" end_page="15" type="concl">
    <SectionTitle>
6. Conclusions
</SectionTitle>
    <Paragraph position="0"> Results reported in the experiment above have ben limited to the ability of the system to cope with what has always ben regarded as the toughest task for an NLP system to cope with. We have not adresed the problem of question answering for lack of space.</Paragraph>
    <Paragraph position="1"> Would it be posible for computers the recognize the layout of a Web page, much in the same maner as a human? Much like the development of the Semantic Web itself, early eforts to integrate natural language technology with the Semantic Web wil no doubt be slow and incremental. By weaving natural language into the basic fabric of the Semantic Web, we can begin to create an enormous network of knowledge easily acesible by both machines and humans alike. Furthermore, we believe that natural language querying capabilities wil be a key component of any future Semantic Web system.</Paragraph>
    <Paragraph position="2"> By providing &amp;quot;natural&amp;quot; means for creating and acesing information on the Semantic Web, we can dramaticaly lower the barier of entry to the Semantic Web. Natural language suport gives users a whole new way of interacting with any information system, and from a knowledge enginering point of view, natural language technology divorces the majority of users from the ned to understand formal ontologies. As we have tried to show in the paper, this cals for beter NLP tols where a lot of efort has to be put in order to alow for complete and shalow techniques to coalesce smothly into one single system. GETARUNS represents such a hybrid system and its performance is steadily improving.</Paragraph>
    <Paragraph position="3"> In the future we intend to adres the problem of using the database of TEs created by our system in asnswering a more extended set of natural language queries than what has ben tried sofar.</Paragraph>
  </Section>
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