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<Paper uid="H05-1092">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 732-739, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Multi-way Relation Classification: Application to Protein-Protein Interactions</Title>
  <Section position="7" start_page="737" end_page="738" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We tackled an important and difficult task, the classification of different interaction types between proteins in text. A solution to this problem would have an impact on a variety of important challenges in modern biology. We used a protein-interaction  database. Ot: sentences for which the annotator found an interaction different from those in Table 2. No: sentences for which the annotator found no interaction. The bottom row shows the accuracy of using the database to label the individual sentences.</Paragraph>
    <Paragraph position="1"> database to automatically gather labeled data for this task, and implemented graphical models that can simultaneously perform protein name tagging and relation identification, achieving high accuracy on both problems. We also found evidence supporting the hypothesis that citation sentences are a good source of training data, most likely because they provide a concise and precise way of summarizing facts in the bioscience literature.</Paragraph>
    <Paragraph position="2"> Acknowledgments. We thank Janice Hamer for her help in labeling examples and other biological insights. This research was supported by a grant from NSF DBI-0317510 and a gift from Genentech.</Paragraph>
  </Section>
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