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<Paper uid="P05-3021">
  <Title>Automating Temporal Annotation with TARSQI</Title>
  <Section position="9" start_page="82" end_page="83" type="concl">
    <SectionTitle>
7 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> The TARSQI system generates temporal information in news texts. The five modules presented here are held together by the TimeML annotation language and add time expressions (GUTime), events (Evita), subordination relations between events (Slinket), local temporal relations between times and events (GUTenLINK), and global temporal relations between times and events (SputLink).</Paragraph>
    <Paragraph position="1">  In the nearby future, we will experiment with more strategies to extract temporal relations from texts. One avenue is to exploit temporal regularities in SLINKs, in effect using the output of Slinket as a means to derive even more TLINKs. We are also compiling more annotated data in order to provide more training data for machine learning approaches to TLINK extraction. SputLink currently uses only qualitative temporal infomation, it will be extended to use quantitative information, allowing it to reason over durations.</Paragraph>
  </Section>
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