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<Paper uid="W04-1209">
  <Title>Support Vector Machine Approach to Extracting Gene References into Function from Biological Documents</Title>
  <Section position="6" start_page="56" end_page="56" type="concl">
    <SectionTitle>
5 Conclusion and Future work
</SectionTitle>
    <Paragraph position="0"> This paper proposed an automatic approach to locate the GeneRIF sentence in an abstract with the assistance of SVMs, reducing the human effort in updating and maintaining the GeneRIF field in the LocusLink database.</Paragraph>
    <Paragraph position="1"> We have to admit that the 139 abstracts provided in TREC 2003 are too few to verify the performance among models, and the results based on these 139 abstracts may be slightly biased. Our next step would aim at measuring the cross-validation performances using Dice coefficient. The syntactic information is worth exploring, since the sentences describing gene functions may share some common structural patterns. Moreover, how the weighting scheme affects the performance is also very interesting. We are currently trying to obtain a weighting scheme that can best distinguish GeneRIF sentence from non-GeneRIF sentence without classifiers.</Paragraph>
  </Section>
class="xml-element"></Paper>