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<Paper uid="W06-1639">
  <Title>floor-debate transcripts</Title>
  <Section position="8" start_page="333" end_page="333" type="concl">
    <SectionTitle>
6 Conclusion and future work
</SectionTitle>
    <Paragraph position="0"> In this study, we focused on very general types of cross-document classification preferences, utilizing constraints based only on speaker identity and on direct textual references between statements. We showed that the integration of even very limited information regarding inter-document relationships can significantly increase the accuracy of support/opposition classification.</Paragraph>
    <Paragraph position="1"> The simple constraints modeled in our study, however, represent just a small portion of the rich network of relationships that connect statements and speakers across the political universe and in the wider realm of opinionated social discourse. One intriguing possibility is to take advantage of (readily identifiable) information regarding interpersonal relationships, making use of speaker/author affiliations, positions within a social hierarchy, and so on. Or, we could even attempt to model relationships between topics or concepts, in a kind of extension of collaborative filtering. For example, perhaps we could infer that two speakers sharing a common opinion on evolutionary biologist Richard Dawkins (a.k.a. &amp;quot;Darwin's rottweiler&amp;quot;) will be likely to agree in a debate centered on Intelligent Design. While such functionality is well beyond the scope of our current study, we are optimistic that we can develop methods to exploit additional types of relationships in future work.</Paragraph>
    <Paragraph position="2"> Acknowledgments We thank Claire Cardie, Jon Kleinberg, Michael Macy, Andrew Myers, and the six anonymous EMNLP referees for valuable discussions and comments. We also thank Reviewer 1 for generously providing additional post hoc feedback, and the EMNLP chairs Eric Gaussier and Dan Jurafsky for facilitating the process (as well as for allowing authors an extra proceedings page...). This paper is based upon work supported in part by the National Science Foundation under grant no. IIS-0329064. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of any sponsoring institutions, the U.S. government, or any other entity.</Paragraph>
  </Section>
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