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<Paper uid="P06-1112">
  <Title>Exploring Correlation of Dependency Relation Paths for Answer Extraction</Title>
  <Section position="10" start_page="895" end_page="895" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we propose a relation path correlation-based method to rank candidate answers in answer extraction. We extract and pair relation paths from questions and candidate sentences. Next, we measure the relation path correlation in each pair based on approximate phrase mapping score and relation sequence alignment, which is calculated by DTW algorithm. Lastly, a ME-based ranking model is proposed to incorporate the path correlations and rank candidate answers. The experiment on TREC questions shows that our method significantly outperforms a density-based method by 50% in MRR and three state-of-the-art syntactic-based methods by up to 20% in MRR. Furthermore, the method is especially effective for difficult questions, for which NER may not help. Therefore, it may be used to further enhance state-of-the-art QA systems even if they have a good NER. In the future, we are to further evaluate the method based on the overall performance of a QA system and adapt it to sentence retrieval task.</Paragraph>
  </Section>
class="xml-element"></Paper>