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Use of Ranked Cross Document Evidence Trails for Hypothesis Generation

Use of Ranked Cross Document Evidence Trails for Hypothesis Generation. Presenter : Jiang-Shan Wang Authors : Rohini K. Srihari, Li Xu, Tushar Saxena. 國立雲林科技大學 National Yunlin University of Science and Technology. SIGKDD 2008. Outline. Motivation Objective Methodology Experiments

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Use of Ranked Cross Document Evidence Trails for Hypothesis Generation

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  1. Use of Ranked Cross Document Evidence Trails for Hypothesis Generation Presenter : Jiang-Shan Wang Authors : Rohini K. Srihari, Li Xu, Tushar Saxena 國立雲林科技大學 National Yunlin University of Science and Technology SIGKDD 2008

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • To improve a problem of traditional search. • Different view form previous approaches.

  4. Objective To present a new framework for generating corpus-specific hypotheses graphs.

  5. Methodology - Overview • Concept chain graphs(CCG) construction. • Graph matching. • Evidence trail generation.

  6. Methodology • Concept extraction and selection • Semantex • Relationship extraction • WordNet

  7. Methodology • Generating concept chains • Markov Chain Model • Transition probability • Generating concept graph • Mehlhorn’s Algorithm

  8. Methodology • Content model construction • Hidden Markov Model • State-specific bigram language model • Emission probability • Transition probability

  9. Methodology • Evidence trail generation • Emission probability • Ranking evidence trails

  10. Experiments Evaluation Results

  11. Conclusion • This approach reducing the effort on analysts in constructing domain models. • Ongoing work: • Algorithm to account for the importance of concepts • Generating evidence trails directly from hypothesis graph candidates • Improving techniques for ranking evidence trails

  12. Comments • Advantage • ... • Drawback • … • Application • Text mining • Graph mining

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