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<Paper uid="W06-3605">
  <Title>Using semantic relations to refine coreference decisions. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods</Title>
  <Section position="5" start_page="38" end_page="39" type="concl">
    <SectionTitle>
4 Conclusions and future work
</SectionTitle>
    <Paragraph position="0"> We have considered the problem of identifying a globally optimal solution among a set of candidate solutions, jointly optimizing several target functions that implement domain criteria. Assuming the solutions are generated incrementally, we have derived a probabilistic search algorithm that aims to identify the globally optimal solution without completing all of the candidate solutions. The algorithm is based on a model of the error in prediction caused by the in- null of sentences in the dataset which have the given number of parses. completeness of a partial solution. Using the model, the order in which partial solutions are explored is defined, as well as a stopping criterion for the algorithm. null We have performed an evaluation using best parse identification as the model problem. The results indicate that the method is capable of combining simple heuristic criteria with a complex regressor, identifying solutions with a very low average rank.</Paragraph>
    <Paragraph position="1"> The crucial component of the method is the model of the error d. Improving the accuracy of the model may potentially further improve the performance of the algorithm, allowing a more accurate stopping criterion and better order in which the parses are completed. We have assumed independence between the scores assigned by the target functions. As a future work, a multivariate model will be considered that takes into account the mutual dependencies of the target functions.</Paragraph>
  </Section>
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