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<Paper uid="E06-1051">
  <Title>Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature</Title>
  <Section position="8" start_page="407" end_page="407" type="concl">
    <SectionTitle>
7 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> The good results obtained using only shallow linguistic features provide a higher baseline against which it is possible to measure improvements obtained usingmethods based on deeplinguistic processing. In the near future, we plan to extend our work in several ways.</Paragraph>
    <Paragraph position="1"> First, we would like to evaluate the contribution of syntactic information to relation extraction from biomedical literature. With this aim, we will integratetheoutputofaparser(possiblytrainedon a domain-specific resource such the Genia Treebank). Second, we plan to test the portability of our model on ACE and MUC data sets. Third, we would like to use a named entity recognizer instead of assuming that entities are already extracted or given by a dictionary. Our long term goal is to populate databases and ontologies by extracting information from large text collections such as Medline.</Paragraph>
  </Section>
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