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A Language Modeling Approach to Tracking

A Language Modeling Approach to Tracking. Martijn Spitters & Wessel Kraaij TNO - TPD. TDT2000 at NIST, Gaithersburg. Outline. Introduction: Filtering vs. Tracking Tracking Model Experiments: stemming Influence of normalisation steps merging training stories Conclusions.

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A Language Modeling Approach to Tracking

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  1. A Language Modeling Approach to Tracking Martijn Spitters & Wessel Kraaij TNO - TPD TDT2000 at NIST, Gaithersburg

  2. Outline • Introduction: • Filtering vs. Tracking • Tracking Model • Experiments: • stemming • Influence of normalisation steps • merging training stories • Conclusions

  3. Adaptive filtering vs. tracking • Adaptive filtering: • initial short topic statement, relevance judgements available ‘along the way’ • Individual threshold adaptation • Evaluation: Utility function • Tracking: • 1-4 training stories, no other relevance information • uniform threshold • Evaluation: Normalized detection Cost

  4. Changes in system • Model based on P(S|T) instead of P(Q|D) ==>reversed orientation • Uniform threshold requires careful normalization • Likelihood ratio • Story length normalization • Gaussian normalization

  5. TREC8 Adaptive filtering

  6. Tracking Model

  7. Normalization Story length Gaussian:

  8. Experiment 1: Stemming

  9. Experiment 2: Normalization

  10. Experiment 3: Merging Influence of the unbiased training story merging method on four TDT2 topics

  11. TDT2000 Evaluation Results (1)

  12. TDT2000 Evaluation Results (2)

  13. Conclusions • simple language models are effective • Normalisation is important • Stemming is important • Unbiased training story merging procedure seems to help when training story lengths differ substantially

  14. Future plans • Apply EM to learn optimal i • Refine background models • Apply expansion techniques • Unsupervised adaptation

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